#model
projmodel_stereo <- lmer(MFQTarg ~ MFQSelf_c * MFQStereo_c *
inoutgroup_effects * ident_c +
(MFQSelf_c|subid),
data = stereo_analysis)
#results
tab_model(projmodel_stereo,
title = "Projection moderated by stereotyping")
| MFQ Targ | |||
|---|---|---|---|
| Predictors | Estimates | CI | p |
| (Intercept) | 4.20 | 4.15 – 4.25 | <0.001 |
| MFQSelf_c | 0.14 | 0.12 – 0.17 | <0.001 |
| MFQStereo_c | 0.07 | 0.06 – 0.09 | <0.001 |
| inoutgroup_effects | 0.24 | 0.13 – 0.34 | <0.001 |
| ident_c | 0.04 | 0.01 – 0.07 | 0.014 |
| MFQSelf_c * MFQStereo_c | -0.03 | -0.04 – -0.02 | <0.001 |
|
MFQSelf_c * inoutgroup_effects |
0.37 | 0.31 – 0.42 | <0.001 |
|
MFQStereo_c * inoutgroup_effects |
0.60 | 0.57 – 0.63 | <0.001 |
| MFQSelf_c * ident_c | 0.01 | -0.01 – 0.02 | 0.445 |
| MFQStereo_c * ident_c | -0.03 | -0.04 – -0.02 | <0.001 |
|
inoutgroup_effects * ident_c |
0.24 | 0.18 – 0.30 | <0.001 |
|
(MFQSelf_c * MFQStereo_c) * inoutgroup_effects |
0.01 | -0.01 – 0.02 | 0.405 |
|
(MFQSelf_c * MFQStereo_c) * ident_c |
-0.01 | -0.01 – -0.00 | 0.005 |
|
(MFQSelf_c inoutgroup_effects) ident_c |
0.00 | -0.03 – 0.03 | 0.804 |
|
(MFQStereo_c inoutgroup_effects) ident_c |
0.01 | -0.01 – 0.03 | 0.212 |
|
(MFQSelf_c * MFQStereo_c * inoutgroup_effects) * ident_c |
-0.00 | -0.01 – 0.01 | 0.956 |
| Random Effects | |||
| σ2 | 1.31 | ||
| τ00 subid | 0.45 | ||
| τ11 subid.MFQSelf_c | 0.08 | ||
| ρ01 subid | -0.02 | ||
| ICC | 0.33 | ||
| N subid | 674 | ||
| Observations | 20045 | ||
| Marginal R2 / Conditional R2 | 0.214 / 0.470 | ||
out_only <- stereo_analysis %>% filter(inoutgroup_effects != .5)
projmodel_stereo_out <- lmer(MFQTarg ~ MFQSelf_c * MFQStereo_c * ident_c +
(MFQSelf_c|subid),
data = out_only)
tab_model(projmodel_stereo_out,
title = "Projection moderated by stereotyping with out-group data only")
| MFQ Targ | |||
|---|---|---|---|
| Predictors | Estimates | CI | p |
| (Intercept) | 4.08 | 4.00 – 4.16 | <0.001 |
| MFQSelf_c | -0.04 | -0.08 – 0.01 | 0.102 |
| MFQStereo_c | -0.23 | -0.25 – -0.20 | <0.001 |
| ident_c | -0.08 | -0.13 – -0.04 | <0.001 |
| MFQSelf_c * MFQStereo_c | -0.03 | -0.04 – -0.02 | <0.001 |
| MFQSelf_c * ident_c | 0.00 | -0.02 – 0.03 | 0.759 |
| MFQStereo_c * ident_c | -0.04 | -0.05 – -0.02 | <0.001 |
|
(MFQSelf_c * MFQStereo_c) * ident_c |
-0.01 | -0.01 – 0.00 | 0.078 |
| Random Effects | |||
| σ2 | 1.79 | ||
| τ00 subid | 0.46 | ||
| τ11 subid.MFQSelf_c | 0.13 | ||
| ρ01 subid | 0.13 | ||
| ICC | 0.29 | ||
| N subid | 334 | ||
| Observations | 9939 | ||
| Marginal R2 / Conditional R2 | 0.052 / 0.331 | ||
This analysis was run using only data for the out-group, meaning the main effect of MFQSelf_c now represents the effect of projection for out-group targets only. The effect is not significant, meaning, in the plot below, that the out-group effect is not different from 0.
initial_study_3way <- effect(term = "MFQSelf_c:inoutgroup_effects:ident_c",
xlevels = list(ident_c = c(-1.78, 0, 1.78),
inoutgroup_effects = c(-.5,.5)),
mod = projmodel_stereo)
initial_study_3way <- as.data.frame(initial_study_3way)
initial_study_3way$Group <- as.factor(initial_study_3way$inoutgroup_effects)
initial_study_3way$Identification <- as.factor(initial_study_3way$ident_c)
ident_label <- c("-1.78" = "Low identification \n (1 SD below)",
"0" = "Average identification",
"1.78" = "High identificatiion \n(1 SD above)")
ggplot(initial_study_3way, aes(MFQSelf_c,
fit,
group = Group,
color = Group)) +
geom_smooth(method = "lm", se = FALSE, size = .7, colour = "black",
aes(linetype = Group)) +
facet_wrap(~Identification, labeller =
labeller(Identification = ident_label)) +
scale_linetype_manual("Group", breaks = c(-.5, .5),
labels = c("Out-group", "In-group"),
values = c("solid",
"dashed")) +
theme_minimal() +
labs(title = "Projection predicted by in-group identification and group membership",
subtitle = "Controlling for stereotyping",
x = "MFQ responses for self",
y = "MFQ responses for target")
ggplot(initial_study_main_group, aes(MFQSelf_c,
fit,
group = Group,
color = Group)) +
geom_smooth(method = "lm", se = FALSE, size = .7, colour = "black",
aes(linetype = Group)) +
scale_linetype_manual("Group", breaks = c(-.5, .5),
labels = c("Out-group", "In-group"),
values = c("solid",
"dashed")) +
theme_minimal() +
labs(title = "Projection predicted by group membership",
subtitle = "Controlling for stereotyping",
x = "MFQ responses for self",
y = "MFQ responses for target")
These results demonstrate that our significant three-way interaction from before is no longer significant, but the main effect of group identification still is significant and still shows counter projection. The three-way is not significant because, regardless of in-group identification, people are still counter projecting to the same extent with their out-group and projecting normally with their in-group.
stereo_analysis %>%
select(subid, par_cand) %>%
unique() %>%
group_by(par_cand) %>%
count() %>%
ungroup() %>%
mutate(total = sum(n),
percent = (n/total)*100)
## # A tibble: 2 x 4
## par_cand n total percent
## <fct> <int> <int> <dbl>
## 1 Clinton 429 674 63.6
## 2 Trump 245 674 36.4
is_cand <- lmer(MFQTarg ~ MFQSelf_c * MFQStereo_c *
inoutgroup_effects * ident_c * par_cand +
(MFQSelf_c|subid),
data = stereo_analysis)
tab_model(is_cand,
title = "Projection moderated by stereotyping")
| MFQ Targ | |||
|---|---|---|---|
| Predictors | Estimates | CI | p |
| (Intercept) | 4.30 | 4.22 – 4.37 | <0.001 |
| MFQSelf_c | 0.10 | 0.06 – 0.14 | <0.001 |
| MFQStereo_c | 0.12 | 0.09 – 0.14 | <0.001 |
| inoutgroup_effects | 0.01 | -0.14 – 0.17 | 0.847 |
| ident_c | 0.10 | 0.06 – 0.15 | <0.001 |
| par_cand [Trump] | -0.17 | -0.30 – -0.04 | 0.009 |
| MFQSelf_c * MFQStereo_c | -0.04 | -0.05 – -0.02 | <0.001 |
|
MFQSelf_c * inoutgroup_effects |
0.51 | 0.44 – 0.59 | <0.001 |
|
MFQStereo_c * inoutgroup_effects |
0.62 | 0.58 – 0.67 | <0.001 |
| MFQSelf_c * ident_c | -0.00 | -0.02 – 0.02 | 0.915 |
| MFQStereo_c * ident_c | -0.02 | -0.03 – -0.01 | 0.002 |
|
inoutgroup_effects * ident_c |
0.15 | 0.05 – 0.25 | 0.003 |
|
MFQSelf_c * par_cand [Trump] |
0.13 | 0.07 – 0.19 | <0.001 |
|
MFQStereo_c * par_cand [Trump] |
-0.10 | -0.13 – -0.06 | <0.001 |
|
inoutgroup_effects * par_cand [Trump] |
0.53 | 0.27 – 0.79 | <0.001 |
|
ident_c * par_cand [Trump] |
-0.08 | -0.14 – -0.01 | 0.033 |
|
(MFQSelf_c * MFQStereo_c) * inoutgroup_effects |
0.06 | 0.04 – 0.08 | <0.001 |
|
(MFQSelf_c * MFQStereo_c) * ident_c |
-0.02 | -0.03 – -0.01 | <0.001 |
|
(MFQSelf_c inoutgroup_effects) ident_c |
0.07 | 0.02 – 0.11 | 0.003 |
|
(MFQStereo_c inoutgroup_effects) ident_c |
0.01 | -0.02 – 0.03 | 0.567 |
|
(MFQSelf_c * MFQStereo_c) * par_cand [Trump] |
-0.01 | -0.03 – 0.01 | 0.474 |
|
(MFQSelf_c inoutgroup_effects) par_cand [Trump] |
-0.38 | -0.50 – -0.26 | <0.001 |
|
(MFQStereo_c inoutgroup_effects) par_cand [Trump] |
-0.10 | -0.17 – -0.03 | 0.007 |
|
(MFQSelf_c * ident_c) * par_cand [Trump] |
-0.01 | -0.05 – 0.02 | 0.364 |
|
(MFQStereo_c * ident_c) * par_cand [Trump] |
-0.00 | -0.02 – 0.02 | 0.822 |
|
(inoutgroup_effects ident_c) par_cand [Trump] |
0.03 | -0.11 – 0.17 | 0.651 |
|
(MFQSelf_c * MFQStereo_c * inoutgroup_effects) * ident_c |
0.03 | 0.02 – 0.04 | <0.001 |
|
(MFQSelf_c * MFQStereo_c * inoutgroup_effects) * par_cand [Trump] |
-0.11 | -0.14 – -0.07 | <0.001 |
|
(MFQSelf_c * MFQStereo_c * ident_c) * par_cand [Trump] |
0.02 | 0.01 – 0.03 | <0.001 |
|
(MFQSelf_c inoutgroup_effects ident_c) * par_cand [Trump] |
-0.03 | -0.09 – 0.03 | 0.370 |
|
(MFQStereo_c inoutgroup_effects ident_c) * par_cand [Trump] |
0.02 | -0.02 – 0.05 | 0.353 |
|
(MFQSelf_c * MFQStereo_c * inoutgroup_effects ident_c) par_cand [Trump] |
-0.03 | -0.05 – -0.01 | 0.001 |
| Random Effects | |||
| σ2 | 1.30 | ||
| τ00 subid | 0.44 | ||
| τ11 subid.MFQSelf_c | 0.08 | ||
| ρ01 subid | 0.03 | ||
| ICC | 0.32 | ||
| N subid | 674 | ||
| Observations | 20045 | ||
| Marginal R2 / Conditional R2 | 0.234 / 0.477 | ||
When we include who the participant voted for with stereotyping as well, we still have the main effect from above. Now, the interaction between target group and in-group identification is significant. There is also an interaction between target group (in or out) and who the participant voted for. However, an interaction between target group, degree of in-group identification, and who they voted for is not significant. There are other significant interactions including stereotyping, but, since we only included it as a moderator to see if the relationship persisted, I didn’t go through them thoroughly.
is_group_cand <- effect(term = "MFQSelf_c:inoutgroup_effects:par_cand",
xlevels = list(inoutgroup_effects = c(-.5,.5),
par_cand = c("Clinton", "Trump")),
mod = is_cand)
is_group_cand <- as.data.frame(is_group_cand)
is_group_cand$Group <- as.factor(is_group_cand$inoutgroup_effects)
ggplot(is_group_cand, aes(MFQSelf_c,
fit,
group = Group,
color = Group)) +
geom_smooth(method = "lm", se = FALSE, size = .7, colour = "black",
aes(linetype = Group)) +
facet_wrap(~par_cand)+
scale_linetype_manual("Group", breaks = c(-.5, .5),
labels = c("Out-group", "In-group"),
values = c("solid",
"dashed")) +
theme_minimal() +
labs(title = "Projection predicted by group membership",
subtitle = "Controlling for stereotyping",
x = "MFQ responses for self",
y = "MFQ responses for target")
Clinton supporters counter project and Trump supporters do not. I’ve checked this a lot and it seems to be correct, I must have messed up in my first year when I analyzed it…
is_3way_cand <- effect(term = "MFQSelf_c:inoutgroup_effects:ident_c",
xlevels = list(ident_c = c(-1.78, 0, 1.78),
inoutgroup_effects = c(-.5,.5)),
mod = is_cand)
is_3way_cand <- as.data.frame(is_3way_cand)
is_3way_cand$Group <- as.factor(is_3way_cand$inoutgroup_effects)
is_3way_cand$Identification <- as.factor(is_3way_cand$ident_c)
ident_label <- c("-1.78" = "Low identification \n (1 SD below)",
"0" = "Average identification",
"1.78" = "High identificatiion \n(1 SD above)")
ggplot(is_3way_cand, aes(MFQSelf_c,
fit,
group = Group,
color = Group)) +
geom_smooth(method = "lm", se = FALSE, size = .7, colour = "black",
aes(linetype = Group)) +
facet_wrap(~Identification, labeller =
labeller(Identification = ident_label)) +
scale_linetype_manual("Group", breaks = c(-.5, .5),
labels = c("Out-group", "In-group"),
values = c("solid",
"dashed")) +
theme_minimal() +
labs(title = "Projection predicted by in-group identification and group membership",
subtitle = "Controlling for stereotyping",
x = "MFQ responses for self",
y = "MFQ responses for target")
ggplot(is_group_cand, aes(MFQSelf_c,
fit,
group = Group,
color = Group)) +
geom_smooth(method = "lm", se = FALSE, size = .7, colour = "black",
aes(linetype = Group)) +
scale_linetype_manual("Group", breaks = c(-.5, .5),
labels = c("Out-group", "In-group"),
values = c("solid",
"dashed")) +
theme_minimal() +
labs(title = "Projection predicted by group membership",
subtitle = "Controlling for stereotyping",
x = "MFQ responses for self",
y = "MFQ responses for target")
#separating MFQ into binding and individualizing subscales
mfq_binding <- clean_data2 %>%
filter(MFQ_Number %in% c("3", "4", "5", "9", "10", "11", "14", "15", "16",
"19", "20", "21", "25", "26", "27", "30", "31", "32")) %>%
select(subid, MFQ_Number, MFQTarg, MFQSelf_c, MFQSelf, MFQStereo_c, MFQStereo, inoutgroup_effects, ident_c, par_cand, Liking, WilltoDiscuss, Valid, Treatment, Contentious) %>%
unique() %>%
na.omit()
mfq_individual <- clean_data2 %>%
filter(MFQ_Number %in% c("1", "2", "7", "8", "12", "13", "17", "18", "23",
"24", "28", "29")) %>%
select(subid, MFQ_Number, MFQTarg, MFQSelf_c, MFQSelf, MFQStereo_c, MFQStereo, inoutgroup_effects, ident_c, par_cand, Liking, WilltoDiscuss, Valid, Treatment, Contentious) %>%
unique() %>%
na.omit()
#model
is_mfqbinding <- lmer(MFQTarg ~ MFQSelf_c * MFQStereo_c *
inoutgroup_effects * ident_c +
(MFQSelf_c|subid),
data = mfq_binding)
#results
tab_model(is_mfqbinding,
title = "Projection moderated by stereotyping for binding morals")
| MFQ Targ | |||
|---|---|---|---|
| Predictors | Estimates | CI | p |
| (Intercept) | 4.19 | 4.12 – 4.27 | <0.001 |
| MFQSelf_c | 0.13 | 0.10 – 0.15 | <0.001 |
| MFQStereo_c | 0.15 | 0.13 – 0.17 | <0.001 |
| inoutgroup_effects | -0.06 | -0.21 – 0.09 | 0.416 |
| ident_c | -0.02 | -0.06 – 0.02 | 0.424 |
| MFQSelf_c * MFQStereo_c | -0.02 | -0.04 – -0.01 | <0.001 |
|
MFQSelf_c * inoutgroup_effects |
0.30 | 0.25 – 0.35 | <0.001 |
|
MFQStereo_c * inoutgroup_effects |
0.30 | 0.26 – 0.34 | <0.001 |
| MFQSelf_c * ident_c | 0.00 | -0.01 – 0.01 | 0.807 |
| MFQStereo_c * ident_c | -0.03 | -0.04 – -0.02 | <0.001 |
|
inoutgroup_effects * ident_c |
0.43 | 0.35 – 0.51 | <0.001 |
|
(MFQSelf_c * MFQStereo_c) * inoutgroup_effects |
-0.04 | -0.06 – -0.02 | <0.001 |
|
(MFQSelf_c * MFQStereo_c) * ident_c |
-0.01 | -0.01 – -0.00 | 0.006 |
|
(MFQSelf_c inoutgroup_effects) ident_c |
0.04 | 0.01 – 0.06 | 0.007 |
|
(MFQStereo_c inoutgroup_effects) ident_c |
0.03 | 0.01 – 0.05 | 0.007 |
|
(MFQSelf_c * MFQStereo_c * inoutgroup_effects) * ident_c |
0.01 | -0.01 – 0.02 | 0.364 |
| Random Effects | |||
| σ2 | 1.00 | ||
| τ00 subid | 0.90 | ||
| τ11 subid.MFQSelf_c | 0.05 | ||
| ρ01 subid | 0.04 | ||
| ICC | 0.50 | ||
| N subid | 669 | ||
| Observations | 11942 | ||
| Marginal R2 / Conditional R2 | 0.189 / 0.593 | ||
is_binding_3way <- effect(term = "MFQSelf_c:inoutgroup_effects:ident_c",
xlevels = list(ident_c = c(-1.78, 0, 1.78),
inoutgroup_effects = c(-.5,.5)),
mod = is_mfqbinding)
is_binding_3way <- as.data.frame(is_binding_3way)
is_binding_3way$Group <- as.factor(is_binding_3way$inoutgroup_effects)
is_binding_3way$Identification <- as.factor(is_binding_3way$ident_c)
ident_label <- c("-1.78" = "Low identification \n (1 SD below)",
"0" = "Average identification",
"1.78" = "High identificatiion \n(1 SD above)")
ggplot(is_binding_3way, aes(MFQSelf_c,
fit,
group = Group,
color = Group)) +
geom_smooth(method = "lm", se = FALSE, size = .7, colour = "black",
aes(linetype = Group)) +
facet_wrap(~Identification, labeller =
labeller(Identification = ident_label)) +
scale_linetype_manual("Group", breaks = c(-.5, .5),
labels = c("Out-group", "In-group"),
values = c("solid",
"dashed")) +
theme_minimal() +
labs(title = "Projection of binding morals predicted by in-group identification and group membership",
subtitle = "Controlling for stereotyping",
x = "MFQ responses for self",
y = "MFQ responses for target")
ggplot(is_binding_group, aes(MFQSelf_c,
fit,
group = Group,
color = Group)) +
geom_smooth(method = "lm", se = FALSE, size = .7, colour = "black",
aes(linetype = Group)) +
scale_linetype_manual("Group", breaks = c(-.5, .5),
labels = c("Out-group", "In-group"),
values = c("solid",
"dashed")) +
theme_minimal() +
labs(title = "Projection of binding morals predicted by group membership",
subtitle = "Controlling for stereotyping",
x = "MFQ responses for self",
y = "MFQ responses for target")
is_mfqbinding_cand <- lmer(MFQTarg ~ MFQSelf_c * MFQStereo_c *
inoutgroup_effects * ident_c * par_cand +
(MFQSelf_c|subid),
data = mfq_binding)
tab_model(is_mfqbinding_cand,
title = "Projection moderated by stereotyping for binding morals")
| MFQ Targ | |||
|---|---|---|---|
| Predictors | Estimates | CI | p |
| (Intercept) | 4.46 | 4.37 – 4.56 | <0.001 |
| MFQSelf_c | 0.12 | 0.09 – 0.15 | <0.001 |
| MFQStereo_c | 0.20 | 0.17 – 0.23 | <0.001 |
| inoutgroup_effects | -0.71 | -0.90 – -0.53 | <0.001 |
| ident_c | 0.12 | 0.06 – 0.18 | <0.001 |
| par_cand [Trump] | -0.59 | -0.75 – -0.43 | <0.001 |
| MFQSelf_c * MFQStereo_c | -0.02 | -0.03 – -0.00 | 0.026 |
|
MFQSelf_c * inoutgroup_effects |
0.33 | 0.26 – 0.40 | <0.001 |
|
MFQStereo_c * inoutgroup_effects |
0.30 | 0.24 – 0.35 | <0.001 |
| MFQSelf_c * ident_c | 0.01 | -0.01 – 0.03 | 0.530 |
| MFQStereo_c * ident_c | -0.02 | -0.04 – -0.00 | 0.033 |
|
inoutgroup_effects * ident_c |
0.19 | 0.07 – 0.31 | 0.002 |
|
MFQSelf_c * par_cand [Trump] |
0.06 | -0.00 – 0.11 | 0.052 |
|
MFQStereo_c * par_cand [Trump] |
-0.15 | -0.19 – -0.10 | <0.001 |
|
inoutgroup_effects * par_cand [Trump] |
1.79 | 1.46 – 2.11 | <0.001 |
|
ident_c * par_cand [Trump] |
-0.10 | -0.18 – -0.01 | 0.028 |
|
(MFQSelf_c * MFQStereo_c) * inoutgroup_effects |
-0.03 | -0.07 – -0.00 | 0.028 |
|
(MFQSelf_c * MFQStereo_c) * ident_c |
-0.01 | -0.02 – -0.00 | 0.003 |
|
(MFQSelf_c inoutgroup_effects) ident_c |
0.06 | 0.02 – 0.10 | 0.008 |
|
(MFQStereo_c inoutgroup_effects) ident_c |
0.03 | -0.00 – 0.07 | 0.059 |
|
(MFQSelf_c * MFQStereo_c) * par_cand [Trump] |
-0.04 | -0.06 – -0.01 | 0.005 |
|
(MFQSelf_c inoutgroup_effects) par_cand [Trump] |
-0.05 | -0.16 – 0.06 | 0.370 |
|
(MFQStereo_c inoutgroup_effects) par_cand [Trump] |
0.03 | -0.06 – 0.12 | 0.531 |
|
(MFQSelf_c * ident_c) * par_cand [Trump] |
-0.03 | -0.06 – 0.00 | 0.098 |
|
(MFQStereo_c * ident_c) * par_cand [Trump] |
0.01 | -0.01 – 0.04 | 0.255 |
|
(inoutgroup_effects ident_c) par_cand [Trump] |
-0.01 | -0.19 – 0.16 | 0.881 |
|
(MFQSelf_c * MFQStereo_c * inoutgroup_effects) * ident_c |
0.01 | -0.01 – 0.03 | 0.283 |
|
(MFQSelf_c * MFQStereo_c * inoutgroup_effects) * par_cand [Trump] |
0.00 | -0.05 – 0.06 | 0.883 |
|
(MFQSelf_c * MFQStereo_c * ident_c) * par_cand [Trump] |
0.02 | 0.01 – 0.03 | 0.003 |
|
(MFQSelf_c inoutgroup_effects ident_c) * par_cand [Trump] |
-0.03 | -0.09 – 0.03 | 0.369 |
|
(MFQStereo_c inoutgroup_effects ident_c) * par_cand [Trump] |
-0.02 | -0.07 – 0.03 | 0.416 |
|
(MFQSelf_c * MFQStereo_c * inoutgroup_effects ident_c) par_cand [Trump] |
-0.01 | -0.04 – 0.02 | 0.524 |
| Random Effects | |||
| σ2 | 0.99 | ||
| τ00 subid | 0.70 | ||
| τ11 subid.MFQSelf_c | 0.04 | ||
| ρ01 subid | 0.06 | ||
| ICC | 0.44 | ||
| N subid | 669 | ||
| Observations | 11942 | ||
| Marginal R2 / Conditional R2 | 0.284 / 0.599 | ||
Main effect of target group is still significant. Who participants voted for is marginal (.050). The interaction between target group and identification is still significant. The interaction between target group, identification, and who they voted for is not significant.
bind_group_cand <- effect(term = "MFQSelf_c:inoutgroup_effects:par_cand",
xlevels = list(inoutgroup_effects = c(-.5,.5),
par_cand = c("Clinton", "Trump")),
mod = is_mfqbinding_cand)
bind_group_cand <- as.data.frame(bind_group_cand)
bind_group_cand$Group <- as.factor(bind_group_cand$inoutgroup_effects)
ggplot(bind_group_cand, aes(MFQSelf_c,
fit,
group = Group,
color = Group)) +
geom_smooth(method = "lm", se = FALSE, size = .7, colour = "black",
aes(linetype = Group)) +
facet_wrap(~par_cand)+
scale_linetype_manual("Group", breaks = c(-.5, .5),
labels = c("Out-group", "In-group"),
values = c("solid",
"dashed")) +
theme_minimal() +
labs(title = "Projection predicted by group membership",
subtitle = "Controlling for stereotyping",
x = "MFQ responses for self",
y = "MFQ responses for target")
is_mfqindividual <- lmer(MFQTarg ~ MFQSelf_c * MFQStereo_c *
inoutgroup_effects * ident_c +
(MFQSelf_c|subid),
data = mfq_individual)
tab_model(is_mfqindividual,
title = "Projection moderated by stereotyping for individualizing morals")
| MFQ Targ | |||
|---|---|---|---|
| Predictors | Estimates | CI | p |
| (Intercept) | 4.16 | 4.08 – 4.23 | <0.001 |
| MFQSelf_c | 0.21 | 0.18 – 0.24 | <0.001 |
| MFQStereo_c | 0.15 | 0.13 – 0.17 | <0.001 |
| inoutgroup_effects | 0.85 | 0.70 – 1.00 | <0.001 |
| ident_c | 0.13 | 0.09 – 0.17 | <0.001 |
| MFQSelf_c * MFQStereo_c | -0.03 | -0.04 – -0.02 | <0.001 |
|
MFQSelf_c * inoutgroup_effects |
0.24 | 0.18 – 0.30 | <0.001 |
|
MFQStereo_c * inoutgroup_effects |
0.30 | 0.25 – 0.35 | <0.001 |
| MFQSelf_c * ident_c | -0.01 | -0.03 – 0.00 | 0.093 |
| MFQStereo_c * ident_c | -0.02 | -0.03 – -0.01 | 0.003 |
|
inoutgroup_effects * ident_c |
-0.06 | -0.14 – 0.03 | 0.186 |
|
(MFQSelf_c * MFQStereo_c) * inoutgroup_effects |
-0.06 | -0.08 – -0.04 | <0.001 |
|
(MFQSelf_c * MFQStereo_c) * ident_c |
0.00 | -0.00 – 0.01 | 0.711 |
|
(MFQSelf_c inoutgroup_effects) ident_c |
0.02 | -0.01 – 0.05 | 0.170 |
|
(MFQStereo_c inoutgroup_effects) ident_c |
0.03 | 0.01 – 0.06 | 0.009 |
|
(MFQSelf_c * MFQStereo_c * inoutgroup_effects) * ident_c |
0.01 | -0.01 – 0.02 | 0.305 |
| Random Effects | |||
| σ2 | 0.87 | ||
| τ00 subid | 0.86 | ||
| τ11 subid.MFQSelf_c | 0.06 | ||
| ρ01 subid | 0.01 | ||
| ICC | 0.54 | ||
| N subid | 669 | ||
| Observations | 7956 | ||
| Marginal R2 / Conditional R2 | 0.240 / 0.649 | ||
The main effect of target group is still significant. The interaction between target group and identification is no longer significant, but I plotted it just to show the difference.
describe(mfq_individual$ident_c)
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 7956 -0.03 1.78 -0.01 -0.04 1.8 -4.23 3.77 8 -0.02 -0.32 0.02
is_indiv_3way <- effect(term = "MFQSelf_c:inoutgroup_effects:ident_c",
xlevels = list(ident_c = c(-1.78, 0, 1.78),
inoutgroup_effects = c(-.5,.5)),
mod = is_mfqindividual)
is_indiv_3way <- as.data.frame(is_indiv_3way)
is_indiv_3way$Group <- as.factor(is_indiv_3way$inoutgroup_effects)
is_indiv_3way$Identification <- as.factor(is_indiv_3way$ident_c)
ident_label <- c("-1.78" = "Low identification \n (1 SD below)",
"0" = "Average identification",
"1.78" = "High identificatiion \n(1 SD above)")
ggplot(is_indiv_3way, aes(MFQSelf_c,
fit,
group = Group,
color = Group)) +
geom_smooth(method = "lm", se = FALSE, size = .7, colour = "black",
aes(linetype = Group)) +
facet_wrap(~Identification, labeller =
labeller(Identification = ident_label)) +
scale_linetype_manual("Group", breaks = c(-.5, .5),
labels = c("Out-group", "In-group"),
values = c("solid",
"dashed")) +
theme_minimal() +
labs(title = "Projection of individualizing morals predicted by in-group identification and group membership",
subtitle = "Controlling for stereotyping",
x = "MFQ responses for self",
y = "MFQ responses for target")
ggplot(is_indiv_group, aes(MFQSelf_c,
fit,
group = Group,
color = Group)) +
geom_smooth(method = "lm", se = FALSE, size = .7, colour = "black",
aes(linetype = Group)) +
scale_linetype_manual("Group", breaks = c(-.5, .5),
labels = c("Out-group", "In-group"),
values = c("solid",
"dashed")) +
theme_minimal() +
labs(title = "Projection of individualizing morals predicted by group membership",
subtitle = "Controlling for stereotyping",
x = "MFQ responses for self",
y = "MFQ responses for target")
#model
is_mfqindividual_cand <- lmer(MFQTarg ~ MFQSelf_c * MFQStereo_c *
inoutgroup_effects * ident_c * par_cand +
(MFQSelf_c|subid),
data = mfq_individual)
#results
tab_model(is_mfqindividual_cand,
title = "Projection moderated by stereotyping for individual morals")
| MFQ Targ | |||
|---|---|---|---|
| Predictors | Estimates | CI | p |
| (Intercept) | 3.92 | 3.82 – 4.02 | <0.001 |
| MFQSelf_c | 0.23 | 0.19 – 0.27 | <0.001 |
| MFQStereo_c | 0.19 | 0.15 – 0.22 | <0.001 |
| inoutgroup_effects | 1.56 | 1.36 – 1.76 | <0.001 |
| ident_c | 0.04 | -0.02 – 0.11 | 0.224 |
| par_cand [Trump] | 0.64 | 0.47 – 0.81 | <0.001 |
| MFQSelf_c * MFQStereo_c | -0.03 | -0.05 – -0.01 | 0.001 |
|
MFQSelf_c * inoutgroup_effects |
0.28 | 0.19 – 0.36 | <0.001 |
|
MFQStereo_c * inoutgroup_effects |
0.15 | 0.07 – 0.22 | <0.001 |
| MFQSelf_c * ident_c | -0.01 | -0.03 – 0.02 | 0.582 |
| MFQStereo_c * ident_c | 0.00 | -0.02 – 0.03 | 0.756 |
|
inoutgroup_effects * ident_c |
0.21 | 0.08 – 0.34 | 0.001 |
|
MFQSelf_c * par_cand [Trump] |
-0.06 | -0.13 – 0.01 | 0.077 |
|
MFQStereo_c * par_cand [Trump] |
-0.03 | -0.09 – 0.02 | 0.244 |
|
inoutgroup_effects * par_cand [Trump] |
-1.80 | -2.14 – -1.46 | <0.001 |
|
ident_c * par_cand [Trump] |
-0.01 | -0.10 – 0.08 | 0.775 |
|
(MFQSelf_c * MFQStereo_c) * inoutgroup_effects |
-0.02 | -0.06 – 0.02 | 0.272 |
|
(MFQSelf_c * MFQStereo_c) * ident_c |
-0.00 | -0.02 – 0.01 | 0.457 |
|
(MFQSelf_c inoutgroup_effects) ident_c |
0.04 | -0.02 – 0.09 | 0.167 |
|
(MFQStereo_c inoutgroup_effects) ident_c |
-0.03 | -0.08 – 0.01 | 0.130 |
|
(MFQSelf_c * MFQStereo_c) * par_cand [Trump] |
-0.02 | -0.05 – 0.01 | 0.184 |
|
(MFQSelf_c inoutgroup_effects) par_cand [Trump] |
-0.06 | -0.21 – 0.08 | 0.370 |
|
(MFQStereo_c inoutgroup_effects) par_cand [Trump] |
0.19 | 0.09 – 0.30 | <0.001 |
|
(MFQSelf_c * ident_c) * par_cand [Trump] |
0.01 | -0.03 – 0.05 | 0.596 |
|
(MFQStereo_c * ident_c) * par_cand [Trump] |
-0.03 | -0.06 – -0.00 | 0.042 |
|
(inoutgroup_effects ident_c) par_cand [Trump] |
-0.03 | -0.21 – 0.15 | 0.746 |
|
(MFQSelf_c * MFQStereo_c * inoutgroup_effects) * ident_c |
0.03 | 0.01 – 0.06 | 0.006 |
|
(MFQSelf_c * MFQStereo_c * inoutgroup_effects) * par_cand [Trump] |
-0.02 | -0.08 – 0.03 | 0.443 |
|
(MFQSelf_c * MFQStereo_c * ident_c) * par_cand [Trump] |
0.01 | -0.00 – 0.03 | 0.157 |
|
(MFQSelf_c inoutgroup_effects ident_c) * par_cand [Trump] |
-0.04 | -0.11 – 0.04 | 0.331 |
|
(MFQStereo_c inoutgroup_effects ident_c) * par_cand [Trump] |
0.07 | 0.01 – 0.13 | 0.016 |
|
(MFQSelf_c * MFQStereo_c * inoutgroup_effects ident_c) par_cand [Trump] |
-0.04 | -0.07 – -0.01 | 0.010 |
| Random Effects | |||
| σ2 | 0.87 | ||
| τ00 subid | 0.69 | ||
| τ11 subid.MFQSelf_c | 0.06 | ||
| ρ01 subid | 0.03 | ||
| ICC | 0.49 | ||
| N subid | 669 | ||
| Observations | 7956 | ||
| Marginal R2 / Conditional R2 | 0.320 / 0.654 | ||
Main effect of target group is still significant. The main effect of who participants voted for is marginal (.077). The interactions between target group and identification between target group and who participants voted for, and target group, identification, and who participants voted for are all not significant.
ggplot(is_indiv_group_cand, aes(MFQSelf_c,
fit,
group = Group,
color = Group)) +
geom_smooth(method = "lm", se = FALSE, size = .7, colour = "black",
aes(linetype = Group)) +
scale_linetype_manual("Group", breaks = c(-.5, .5),
labels = c("Out-group", "In-group"),
values = c("solid",
"dashed")) +
theme_minimal() +
labs(title = "Projection of individualizing morals predicted by group membership",
subtitle = "Controlling for stereotyping",
x = "MFQ responses for self",
y = "MFQ responses for target")
#separating MFQ into binding and individualizing subscales
f1_mfq_binding <- pptnpf1_comb %>%
filter(number %in% c("3", "4", "5", "9", "10", "11", "14", "15", "16",
"20", "21", "22")) %>%
select(Subject, number, Condition, MFQSelf_c, MFQSelf, Identification_c, Gender, Race, MFQTarg) %>%
unique() %>%
na.omit()
f1_mfq_individual <- pptnpf1_comb %>%
filter(number %in% c("1", "2", "7", "8", "12", "13", "18", "19")) %>%
select(Subject, number, Condition, MFQSelf_c, MFQSelf, Identification_c, Gender, Race, MFQTarg) %>%
unique() %>%
na.omit()
f1_binding <- lmer(MFQTarg ~ MFQSelf_c*Identification_c*Condition +
(MFQSelf_c|Subject),
data = f1_mfq_binding, na.action = na.omit)
tab_model(f1_binding,
title = "Effects of predictors and interactions on projection of binding morals")
| MFQ Targ | |||
|---|---|---|---|
| Predictors | Estimates | CI | p |
| (Intercept) | 4.35 | 4.25 – 4.45 | <0.001 |
| MFQSelf_c | 0.12 | 0.06 – 0.17 | <0.001 |
| Identification_c | 0.19 | 0.10 – 0.28 | <0.001 |
| ConditionPRWvPB | -0.02 | -0.09 – 0.05 | 0.596 |
| ConditionPRvW | 0.07 | -0.01 – 0.15 | 0.097 |
| ConditionPvR | -0.07 | -0.22 – 0.08 | 0.392 |
|
MFQSelf_c * Identification_c |
-0.08 | -0.13 – -0.03 | 0.001 |
|
MFQSelf_c : ConditionPRWvPB |
0.07 | 0.04 – 0.11 | <0.001 |
| MFQSelf_c : ConditionPRvW | 0.13 | 0.09 – 0.17 | <0.001 |
| MFQSelf_c : ConditionPvR | 0.15 | 0.07 – 0.23 | <0.001 |
|
Identification_c : ConditionPRWvPB |
-0.06 | -0.11 – -0.01 | 0.027 |
|
Identification_c : ConditionPRvW |
-0.05 | -0.12 – 0.02 | 0.196 |
|
Identification_c : ConditionPvR |
0.25 | 0.11 – 0.39 | <0.001 |
|
MFQSelf_c : Identification_c : ConditionPRWvPB |
0.03 | 0.00 – 0.05 | 0.024 |
|
MFQSelf_c : Identification_c : ConditionPRvW |
0.02 | -0.02 – 0.06 | 0.257 |
|
MFQSelf_c : Identification_c : ConditionPvR |
0.07 | 0.00 – 0.14 | 0.047 |
| Random Effects | |||
| σ2 | 1.24 | ||
| τ00 Subject | 0.50 | ||
| τ11 Subject.MFQSelf_c | 0.09 | ||
| ρ01 Subject | -0.13 | ||
| ICC | 0.37 | ||
| N Subject | 306 | ||
| Observations | 3057 | ||
| Marginal R2 / Conditional R2 | 0.100 / 0.436 | ||
#running the plot
ggpreview <- function(...) {
fname <- tempfile(fileext = ".png")
ggsave(filename = fname, ...)
system2("open", fname)
invisible(NULL)
}
knitr::opts_chunk$set(fig.width = 8, fig.height = 6)
ggplot(f1_binding_summary_3way, aes(x = MFQSelf_c,
y = lsmean,
group = as.factor(Condition))) +
facet_grid(.~Identification_c,
labeller = labeller(Identification_c = Ident_label)) +
geom_line(size = .7, aes(linetype = Condition)) +
xlab("MFQ responses for self") +
ylab("MFQ responses for target") +
scale_linetype_manual("Issue polarization",
breaks = c(1,2,3,4),
labels = c ("Political\ncandidate",
"Racial\ndisadvantage",
"Marijuana\nlegalization",
"Peanut\nbutter"),
values = c("solid",
"twodash",
"dotted",
"dashed")) +
ggtitle("Projection predicted by identification and issue polarization") +
theme_minimal(base_size = 13) +
theme(legend.text = element_text(size = 10),
legend.key.width = unit(1, "cm"),
legend.position = "bottom")
ggpreview(width = 8, height = 6, units = "in")
Not much different than the overall, except now average identifiers in the political condition are negatively projecting and people in the racial disadvantage condition who highly identify aren’t projecting at all.
knitr::opts_chunk$set(fig.width = 8, fig.height = 6)
ggplot(f1_binding_sum_cond, aes(x = MFQSelf_c,
y = lsmean,
group = as.factor(Condition)))+
geom_line(size = 1, aes(linetype = Condition)) +
xlab("MFQ responses for self") +
ylab("MFQ responses for target") +
scale_linetype_manual("Issue polarization",
breaks = c(1,2,3,4),
labels = c("Political\ncandidate",
"Racial\ndisadvantage",
"Marijuana\nlegalization",
"Peanut\nbutter"),
values = c("solid",
"twodash",
"dotted",
"dashed")) +
ggtitle("Effect of issue polarization on projection with out-group targets") +
theme_minimal(base_size = 13) +
theme(legend.text = element_text(size = 10),
legend.key.width = unit(1.2, "cm"))
ggpreview(width = 8, height = 6, units = "in")
Only difference is now there is counter projection in the political candidate condition.
knitr::opts_chunk$set(fig.width = 8, fig.height = 6)
ggplot(f1_binding_ident, aes(MFQSelf_c,
fit,
group = Identification)) +
geom_line(aes(linetype = Identification), size = .7) +
scale_linetype_manual("Identification",
breaks = c(-1.19, 0, 1.19),
labels = c ("Low (1 SD below)",
"Average ",
"High (1 SD above)"),
values = c("solid",
"twodash",
"dotted")) +
theme_minimal(base_size = 13) +
labs(title = "Projection predicted by degree of in-group identification",
x = "MFQ responses for self",
y = "MFQ responses for target")
ggpreview(width = 8, height = 6, units = "in")
Pretty similar to the overall model.
f1_individual <- lmer(MFQTarg ~ MFQSelf_c*Identification_c*Condition +
(MFQSelf_c|Subject),
data = f1_mfq_individual, na.action = na.omit)
tab_model(f1_individual,
title = "Effects of predictors and interactions on projection of individualizing morals")
| MFQ Targ | |||
|---|---|---|---|
| Predictors | Estimates | CI | p |
| (Intercept) | 4.13 | 4.03 – 4.22 | <0.001 |
| MFQSelf_c | 0.07 | 0.01 – 0.13 | 0.033 |
| Identification_c | 0.07 | -0.01 – 0.16 | 0.095 |
| ConditionPRWvPB | 0.03 | -0.03 – 0.09 | 0.316 |
| ConditionPRvW | 0.13 | 0.06 – 0.20 | 0.001 |
| ConditionPvR | -0.01 | -0.15 – 0.13 | 0.850 |
|
MFQSelf_c * Identification_c |
-0.15 | -0.21 – -0.09 | <0.001 |
|
MFQSelf_c : ConditionPRWvPB |
0.10 | 0.05 – 0.14 | <0.001 |
| MFQSelf_c : ConditionPRvW | 0.17 | 0.12 – 0.22 | <0.001 |
| MFQSelf_c : ConditionPvR | 0.22 | 0.13 – 0.32 | <0.001 |
|
Identification_c : ConditionPRWvPB |
-0.01 | -0.06 – 0.04 | 0.626 |
|
Identification_c : ConditionPRvW |
0.01 | -0.06 – 0.07 | 0.870 |
|
Identification_c : ConditionPvR |
0.25 | 0.12 – 0.38 | <0.001 |
|
MFQSelf_c : Identification_c : ConditionPRWvPB |
0.04 | 0.01 – 0.07 | 0.014 |
|
MFQSelf_c : Identification_c : ConditionPRvW |
0.09 | 0.04 – 0.13 | <0.001 |
|
MFQSelf_c : Identification_c : ConditionPvR |
0.02 | -0.07 – 0.11 | 0.681 |
| Random Effects | |||
| σ2 | 1.29 | ||
| τ00 Subject | 0.37 | ||
| τ11 Subject.MFQSelf_c | 0.17 | ||
| ρ01 Subject | 0.06 | ||
| ICC | 0.38 | ||
| N Subject | 306 | ||
| Observations | 2446 | ||
| Marginal R2 / Conditional R2 | 0.132 / 0.460 | ||
The results of this model are also not that different from the overall model: the main effects of condition and identification are still significant, as well as the interaction between condition and identification.
#running the plot
ggpreview <- function(...) {
fname <- tempfile(fileext = ".png")
ggsave(filename = fname, ...)
system2("open", fname)
invisible(NULL)
}
knitr::opts_chunk$set(fig.width = 8, fig.height = 6)
ggplot(f1_individual_summary_3way, aes(x = MFQSelf_c,
y = lsmean,
group = as.factor(Condition))) +
facet_grid(.~Identification_c,
labeller = labeller(Identification_c = Ident_label)) +
geom_line(size = .7, aes(linetype = Condition)) +
xlab("MFQ responses for self") +
ylab("MFQ responses for target") +
scale_linetype_manual("Issue polarization",
breaks = c(1,2,3,4),
labels = c ("Political\ncandidate",
"Racial\ndisadvantage",
"Marijuana\nlegalization",
"Peanut\nbutter"),
values = c("solid",
"twodash",
"dotted",
"dashed")) +
ggtitle("Projection predicted by identification and issue polarization") +
theme_minimal(base_size = 13) +
theme(legend.text = element_text(size = 10),
legend.key.width = unit(1, "cm"),
legend.position = "bottom")
ggpreview(width = 8, height = 6, units = "in")
Interesting! With individualizing morals, people counter project more in the political condition when they are average identified than in the overall model and people strongly counter project in both the political and racial disadvantage conditions when highly identified. The overall model did not show counter projection in the racial disadvantage condition when people were highly identified.
knitr::opts_chunk$set(fig.width = 8, fig.height = 6)
ggplot(f1_indiv_sum_cond, aes(x = MFQSelf_c,
y = lsmean,
group = as.factor(Condition)))+
geom_line(size = 1, aes(linetype = Condition)) +
xlab("MFQ responses for self") +
ylab("MFQ responses for target") +
scale_linetype_manual("Issue polarization",
breaks = c(1,2,3,4),
labels = c("Political\ncandidate",
"Racial\ndisadvantage",
"Marijuana\nlegalization",
"Peanut\nbutter"),
values = c("solid",
"twodash",
"dotted",
"dashed")) +
ggtitle("Effect of issue polarization on projection with out-group targets") +
theme_minimal(base_size = 13) +
theme(legend.text = element_text(size = 10),
legend.key.width = unit(1.2, "cm"))
ggpreview(width = 8, height = 6, units = "in")
Only difference is now there is counter projection in the political candidate condition and the racial disadvantage condition is pretty flat.
knitr::opts_chunk$set(fig.width = 8, fig.height = 6)
ggplot(f1_indiv_ident, aes(MFQSelf_c,
fit,
group = Identification)) +
geom_line(aes(linetype = Identification), size = .7) +
scale_linetype_manual("Identification",
breaks = c(-1.19, 0, 1.19),
labels = c ("Low (1 SD below)",
"Average ",
"High (1 SD above)"),
values = c("solid",
"twodash",
"dotted")) +
theme_minimal(base_size = 13) +
labs(title = "Projection predicted by degree of in-group identification",
x = "MFQ responses for self",
y = "MFQ responses for target")
ggpreview(width = 8, height = 6, units = "in")
Pretty similar to the original.
Did this previously in another document, which I have uploaded to this project.
Look at whether or not they agreed or disagreed with the issue AND if the issue is generally liberal or conservative.
Condition: What issue participants were given; 1 = political candidate, 2 = racial discrimination, 3 = weed, 4 = PB
Issue_agreement: 1 for condition 1 is Clinton, 2 is Trump, for other three conditions 1 is yes to agreeing with issue, 4 is no to agreeing with issue (AKA ignoring PB, 1 = liberal, 4 = conservative beliefs)
f1_ideology_model <- lmer(MFQTarg ~ MFQSelf_c*Identification_c*Condition*Issue_agreement +
(MFQSelf_c|Subject),
data = pptnpf1_comb, na.action = na.omit)
tab_model(f1_ideology_model,
title = "Projection predicted by ideology")
| MFQ Targ | |||
|---|---|---|---|
| Predictors | Estimates | CI | p |
| (Intercept) | 4.39 | 4.23 – 4.55 | <0.001 |
| MFQSelf_c | 0.01 | -0.08 – 0.11 | 0.764 |
| Identification_c | 0.30 | 0.15 – 0.44 | <0.001 |
| ConditionPRWvPB | -0.06 | -0.17 – 0.06 | 0.325 |
| ConditionPRvW | 0.26 | 0.14 – 0.39 | <0.001 |
| ConditionPvR | -0.10 | -0.33 – 0.13 | 0.417 |
| Issue_agreement | -0.13 | -0.22 – -0.05 | 0.003 |
|
MFQSelf_c * Identification_c |
-0.14 | -0.23 – -0.05 | 0.002 |
|
MFQSelf_c : ConditionPRWvPB |
0.16 | 0.09 – 0.23 | <0.001 |
| MFQSelf_c : ConditionPRvW | 0.13 | 0.06 – 0.21 | 0.001 |
| MFQSelf_c : ConditionPvR | 0.29 | 0.15 – 0.43 | <0.001 |
|
Identification_c : ConditionPRWvPB |
-0.14 | -0.23 – -0.05 | 0.002 |
|
Identification_c : ConditionPRvW |
-0.12 | -0.23 – -0.01 | 0.038 |
|
Identification_c : ConditionPvR |
0.13 | -0.09 – 0.34 | 0.247 |
|
MFQSelf_c * Issue_agreement |
0.07 | 0.01 – 0.12 | 0.016 |
|
Identification_c * Issue_agreement |
-0.15 | -0.22 – -0.07 | <0.001 |
|
ConditionPRWvPB * Issue_agreement |
0.05 | 0.01 – 0.10 | 0.015 |
|
ConditionPRvW * Issue_agreement |
-0.10 | -0.18 – -0.03 | 0.006 |
|
ConditionPvR * Issue_agreement |
0.04 | -0.07 – 0.15 | 0.506 |
|
MFQSelf_c : Identification_c : ConditionPRWvPB |
0.04 | -0.01 – 0.09 | 0.098 |
|
MFQSelf_c : Identification_c : ConditionPRvW |
0.08 | 0.01 – 0.15 | 0.025 |
|
MFQSelf_c : Identification_c : ConditionPvR |
-0.00 | -0.14 – 0.13 | 0.943 |
|
(MFQSelf_c Identification_c) Issue_agreement |
0.03 | -0.02 – 0.08 | 0.187 |
|
(MFQSelf_c ConditionPRWvPB) Issue_agreement |
-0.04 | -0.07 – -0.01 | 0.002 |
|
(MFQSelf_c ConditionPRvW) Issue_agreement |
0.00 | -0.04 – 0.05 | 0.855 |
|
(MFQSelf_c ConditionPvR) Issue_agreement |
-0.06 | -0.13 – 0.02 | 0.139 |
|
(Identification_c ConditionPRWvPB) Issue_agreement |
0.07 | 0.03 – 0.10 | <0.001 |
|
(Identification_c ConditionPRvW) Issue_agreement |
0.04 | -0.02 – 0.10 | 0.217 |
|
(Identification_c ConditionPvR) Issue_agreement |
0.09 | -0.01 – 0.20 | 0.086 |
|
(MFQSelf_c Identification_c ConditionPRWvPB) * Issue_agreement |
-0.01 | -0.03 – 0.01 | 0.336 |
|
(MFQSelf_c Identification_c ConditionPRvW) * Issue_agreement |
-0.01 | -0.05 – 0.03 | 0.580 |
|
(MFQSelf_c Identification_c ConditionPvR) * Issue_agreement |
0.02 | -0.05 – 0.08 | 0.626 |
| Random Effects | |||
| σ2 | 1.32 | ||
| τ00 Subject | 0.38 | ||
| τ11 Subject.MFQSelf_c | 0.12 | ||
| ρ01 Subject | 0.04 | ||
| ICC | 0.33 | ||
| N Subject | 306 | ||
| Observations | 6115 | ||
| Marginal R2 / Conditional R2 | 0.132 / 0.423 | ||
describe(pptnpf1_comb$Identification_c)
## vars n mean sd median trimmed mad min max range skew kurtosis
## X1 1 6115 -0.01 1.19 -0.03 0 1.48 -2.03 1.97 4 -0.22 -0.72
## se
## X1 0.02
effects_polident_f1 <- effect(term="MFQSelf_c:Identification_c:Condition",
xlevels= list(Identification_c=c(-1.19, 0, 1.19),
Condition=c("1",
"2",
"3",
"4")),
mod=f1_ideology_model)
effects_polident_f1 <- as.data.frame(effects_polident_f1)
effects_polident_f1$Identification<-as.factor(effects_polident_f1$Identification_c)
f1_polident_ident_label <- c("-1.19"="Low Identification \n(1 SD Below)",
"0" = "Average \nIdentification",
"1.19"="High Identification \n(1 SD Above)")
ggplot(effects_polident_f1, aes(MFQSelf_c, fit)) +
geom_line(size = 1, aes(linetype = Condition)) +
facet_wrap(~Identification,
labeller = labeller(Identification=f1_polident_ident_label)) +
scale_linetype_manual("Issue polarization",
breaks = c(1,2,3,4),
labels = c ("Political\ncandidate",
"Racial\ndisadvantage",
"Marijuana\nlegalization",
"Peanut\nbutter"),
values = c("solid",
"twodash",
"dotted",
"dashed")) +
theme_minimal(base_size = 13) +
theme(legend.text = element_text(size = 10),
legend.key.width = unit(1.2, "cm")) +
labs(title = "Projection predicted by in-group identification and condition",
subtitle = "Included issue agreement in this model",
x = "MFQ ratings for self",
y = "MFQ ratings for target")
describe(pptnpf1_comb$Issue_agreement)
## vars n mean sd median trimmed mad min max range skew kurtosis se
## X1 1 6115 1.84 1.35 1 1.67 0 1 4 3 0.99 -1.03 0.02
effects_polident_f1_plot2 <- effect(term="MFQSelf_c:Condition:Issue_agreement",
xlevels= list(Issue_agreement=c(1, 4),
Condition=c("1",
"2",
"3",
"4")),
mod=f1_ideology_model)
effects_polident_f1_plot2 <- as.data.frame(effects_polident_f1_plot2)
effects_polident_f1_plot2$Agreement<-as.factor(effects_polident_f1_plot2$Issue_agreement)
f1_polident_ident_label2 <- c("1" = "Support Clinton, Acknowledge racism, \n Weed legalization, and Smooth PB",
"4" = "Support Trump, Did not acknowledge racism, \n disagree with weed legalization, like crunchy peanut butter")
ggplot(effects_polident_f1_plot2, aes(MFQSelf_c, fit)) +
geom_line(size = 1, aes(linetype = Condition)) +
facet_wrap(~Agreement,
labeller = labeller(Agreement=f1_polident_ident_label2)) +
scale_linetype_manual("Issue polarization",
breaks = c(1,2,3,4),
labels = c ("Political\ncandidate",
"Racial\ndisadvantage",
"Marijuana\nlegalization",
"Peanut\nbutter"),
values = c("solid",
"twodash",
"dotted",
"dashed")) +
theme_minimal(base_size = 13) +
theme(legend.text = element_text(size = 10),
legend.key.width = unit(1.2, "cm")) +
labs(title = "Projection predicted by issue condition and issue agreement",
x = "MFQ ratings for self",
y = "MFQ ratings for target")
contrasts(npf2_clean$condition) <- "contr.helmert"
f2_ideology_model <- lmer(targ ~ self_c*ident_c*condition*opin +
(self_c|sub_id),
data = npf2_clean, na.action = na.omit)
tab_model(f2_ideology_model,
title = "Projection predicted by ideology")
| targ | |||
|---|---|---|---|
| Predictors | Estimates | CI | p |
| (Intercept) | 3.14 | 3.07 – 3.21 | <0.001 |
| self_c | 0.03 | -0.03 – 0.09 | 0.280 |
| ident_c | -0.07 | -0.15 – 0.00 | 0.061 |
| condition1 | 0.02 | -0.08 – 0.12 | 0.728 |
| condition2 | 0.09 | 0.04 – 0.15 | 0.001 |
| condition3 | -0.00 | -0.04 – 0.03 | 0.849 |
| opin | 0.03 | -0.00 – 0.07 | 0.060 |
| self_c * ident_c | -0.11 | -0.19 – -0.04 | 0.001 |
| self_c : condition1 | -0.05 | -0.13 – 0.04 | 0.308 |
| self_c : condition2 | 0.04 | -0.01 – 0.09 | 0.161 |
| self_c : condition3 | -0.01 | -0.04 – 0.02 | 0.575 |
| ident_c : condition1 | -0.01 | -0.13 – 0.11 | 0.882 |
| ident_c : condition2 | 0.06 | -0.00 – 0.12 | 0.064 |
| ident_c : condition3 | 0.01 | -0.03 – 0.06 | 0.481 |
| self_c * opin | -0.01 | -0.05 – 0.02 | 0.387 |
| ident_c * opin | -0.00 | -0.05 – 0.04 | 0.912 |
| condition1 * opin | 0.01 | -0.04 – 0.05 | 0.827 |
| condition2 * opin | -0.02 | -0.05 – 0.01 | 0.210 |
| condition3 * opin | -0.00 | -0.02 – 0.02 | 0.684 |
|
self_c : ident_c : condition1 |
0.08 | -0.03 – 0.19 | 0.144 |
|
self_c : ident_c : condition2 |
0.05 | -0.01 – 0.11 | 0.110 |
|
self_c : ident_c : condition3 |
0.01 | -0.03 – 0.05 | 0.545 |
| (self_c * ident_c) * opin | 0.04 | -0.01 – 0.08 | 0.089 |
|
(self_c * condition1) * opin |
0.01 | -0.03 – 0.05 | 0.663 |
|
(self_c * condition2) * opin |
-0.00 | -0.03 – 0.03 | 0.881 |
|
(self_c * condition3) * opin |
0.01 | -0.01 – 0.03 | 0.228 |
|
(ident_c * condition1) * opin |
-0.00 | -0.07 – 0.07 | 0.960 |
|
(ident_c * condition2) * opin |
-0.04 | -0.08 – -0.00 | 0.032 |
|
(ident_c * condition3) * opin |
0.00 | -0.02 – 0.02 | 0.994 |
|
(self_c * ident_c condition1) opin |
-0.03 | -0.09 – 0.02 | 0.254 |
|
(self_c * ident_c condition2) opin |
-0.04 | -0.08 – 0.00 | 0.062 |
|
(self_c * ident_c condition3) opin |
-0.00 | -0.02 – 0.02 | 0.808 |
| Random Effects | |||
| σ2 | 1.03 | ||
| τ00 sub_id | 0.05 | ||
| τ11 sub_id.self_c | 0.04 | ||
| ρ01 sub_id | 0.53 | ||
| ICC | 0.10 | ||
| N sub_id | 334 | ||
| Observations | 6324 | ||
| Marginal R2 / Conditional R2 | 0.025 / 0.125 | ||
Model failed to converge, so results are unreliable. Any plot using intercepts and slopes from this model would also be unreliable.
dem_analysis <- clean_data2 %>%
select(subid, MFQ_Number, MFQSelf_c, MFQStereo_c, MFQTarg, ident_c, PT_effects,
inoutgroup_effects, par_cand, Race) %>%
unique() %>%
na.omit()
projmodel_stereo_race <- lmer(MFQTarg ~ MFQSelf_c * MFQStereo_c *
inoutgroup_effects * ident_c * Race +
(MFQSelf_c|subid),
data = dem_analysis)
tab_model(projmodel_stereo_race,
title = "Projection moderated by stereotyping and race")
| MFQ Targ | |||
|---|---|---|---|
| Predictors | Estimates | CI | p |
| (Intercept) | 3.25 | 2.39 – 4.11 | <0.001 |
| MFQSelf_c | 0.60 | 0.18 – 1.02 | 0.005 |
| MFQStereo_c | -0.47 | -0.72 – -0.21 | <0.001 |
| inoutgroup_effects | 1.52 | -0.12 – 3.15 | 0.069 |
| ident_c | 0.04 | -0.50 – 0.59 | 0.883 |
| Race [Asian] | 0.79 | -0.10 – 1.68 | 0.083 |
| Race [Black] | 0.96 | 0.07 – 1.85 | 0.034 |
| Race [Latinx] | 1.12 | 0.23 – 2.02 | 0.014 |
| Race [Middle Eastern] | -0.21 | -2.10 – 1.68 | 0.826 |
| Race [Caucasian] | 0.95 | 0.09 – 1.81 | 0.031 |
| Race [Multiracial] | 1.19 | 0.28 – 2.11 | 0.010 |
| Race [Other] | 0.70 | -0.64 – 2.04 | 0.309 |
| MFQSelf_c * MFQStereo_c | 0.08 | -0.03 – 0.20 | 0.154 |
|
MFQSelf_c * inoutgroup_effects |
0.20 | -0.61 – 1.00 | 0.629 |
|
MFQStereo_c * inoutgroup_effects |
1.10 | 0.58 – 1.62 | <0.001 |
| MFQSelf_c * ident_c | -0.15 | -0.45 – 0.14 | 0.300 |
| MFQStereo_c * ident_c | 0.09 | -0.16 – 0.33 | 0.489 |
|
inoutgroup_effects * ident_c |
0.32 | -0.02 – 0.66 | 0.064 |
| MFQSelf_c * Race [Asian] | -0.53 | -0.97 – -0.10 | 0.016 |
| MFQSelf_c * Race [Black] | -0.48 | -0.91 – -0.05 | 0.030 |
| MFQSelf_c * Race [Latinx] | -0.44 | -0.87 – -0.00 | 0.048 |
|
MFQSelf_c * Race [Middle Eastern] |
-1.02 | -2.20 – 0.17 | 0.093 |
|
MFQSelf_c * Race [Caucasian] |
-0.45 | -0.87 – -0.03 | 0.034 |
|
MFQSelf_c * Race [Multiracial] |
-0.52 | -0.96 – -0.07 | 0.023 |
| MFQSelf_c * Race [Other] | -0.36 | -1.09 – 0.37 | 0.334 |
|
MFQStereo_c * Race [Asian] |
0.63 | 0.36 – 0.89 | <0.001 |
|
MFQStereo_c * Race [Black] |
0.54 | 0.28 – 0.80 | <0.001 |
|
MFQStereo_c * Race [Latinx] |
0.50 | 0.23 – 0.76 | <0.001 |
|
MFQStereo_c * Race [Middle Eastern] |
0.12 | -0.74 – 0.97 | 0.790 |
|
MFQStereo_c * Race [Caucasian] |
0.54 | 0.29 – 0.80 | <0.001 |
|
MFQStereo_c * Race [Multiracial] |
0.60 | 0.32 – 0.87 | <0.001 |
|
MFQStereo_c * Race [Other] |
-0.09 | -0.63 – 0.45 | 0.754 |
|
inoutgroup_effects * Race [Asian] |
-1.14 | -2.85 – 0.56 | 0.188 |
|
inoutgroup_effects * Race [Black] |
-0.98 | -2.68 – 0.72 | 0.257 |
|
inoutgroup_effects * Race [Latinx] |
-1.11 | -2.82 – 0.60 | 0.203 |
|
inoutgroup_effects * Race [Caucasian] |
-1.32 | -2.96 – 0.33 | 0.116 |
|
inoutgroup_effects * Race [Multiracial] |
-1.20 | -3.00 – 0.61 | 0.193 |
| ident_c * Race [Asian] | -0.00 | -0.57 – 0.56 | 0.987 |
| ident_c * Race [Black] | -0.06 | -0.63 – 0.51 | 0.834 |
| ident_c * Race [Latinx] | 0.04 | -0.52 – 0.60 | 0.893 |
|
ident_c * Race [Caucasian] |
-0.01 | -0.55 – 0.54 | 0.981 |
|
ident_c * Race [Multiracial] |
0.05 | -0.55 – 0.65 | 0.869 |
| ident_c * Race [Other] | 0.21 | -0.44 – 0.87 | 0.521 |
|
(MFQSelf_c * MFQStereo_c) * inoutgroup_effects |
-0.18 | -0.43 – 0.06 | 0.147 |
|
(MFQSelf_c * MFQStereo_c) * ident_c |
-0.03 | -0.15 – 0.08 | 0.576 |
|
(MFQSelf_c inoutgroup_effects) ident_c |
0.05 | -0.11 – 0.21 | 0.563 |
|
(MFQStereo_c inoutgroup_effects) ident_c |
-0.02 | -0.11 – 0.07 | 0.670 |
|
(MFQSelf_c * MFQStereo_c) * Race [Asian] |
-0.05 | -0.18 – 0.07 | 0.388 |
|
(MFQSelf_c * MFQStereo_c) * Race [Black] |
-0.13 | -0.25 – -0.01 | 0.038 |
|
(MFQSelf_c * MFQStereo_c) * Race [Latinx] |
-0.10 | -0.22 – 0.02 | 0.117 |
|
(MFQSelf_c * MFQStereo_c) * Race [Middle Eastern] |
-0.14 | -1.63 – 1.35 | 0.855 |
|
(MFQSelf_c * MFQStereo_c) * Race [Caucasian] |
-0.11 | -0.23 – 0.00 | 0.060 |
|
(MFQSelf_c * MFQStereo_c) * Race [Multiracial] |
-0.19 | -0.31 – -0.06 | 0.003 |
|
(MFQSelf_c * MFQStereo_c) * Race [Other] |
-0.14 | -0.37 – 0.09 | 0.231 |
|
(MFQSelf_c inoutgroup_effects) Race [Asian] |
0.12 | -0.72 – 0.95 | 0.786 |
|
(MFQSelf_c inoutgroup_effects) Race [Black] |
0.09 | -0.75 – 0.93 | 0.836 |
|
(MFQSelf_c inoutgroup_effects) Race [Latinx] |
0.13 | -0.71 – 0.97 | 0.770 |
|
(MFQSelf_c inoutgroup_effects) Race [Caucasian] |
0.17 | -0.63 – 0.98 | 0.672 |
|
(MFQSelf_c inoutgroup_effects) Race [Multiracial] |
0.11 | -0.77 – 1.00 | 0.807 |
|
(MFQStereo_c inoutgroup_effects) Race [Asian] |
-0.46 | -0.99 – 0.08 | 0.093 |
|
(MFQStereo_c inoutgroup_effects) Race [Black] |
-0.68 | -1.21 – -0.15 | 0.012 |
|
(MFQStereo_c inoutgroup_effects) Race [Latinx] |
-0.68 | -1.22 – -0.15 | 0.012 |
|
(MFQStereo_c inoutgroup_effects) Race [Caucasian] |
-0.48 | -0.99 – 0.04 | 0.071 |
|
(MFQStereo_c inoutgroup_effects) Race [Multiracial] |
-0.52 | -1.09 – 0.05 | 0.073 |
|
(MFQSelf_c * ident_c) * Race [Asian] |
0.18 | -0.12 – 0.48 | 0.249 |
|
(MFQSelf_c * ident_c) * Race [Black] |
0.14 | -0.17 – 0.44 | 0.378 |
|
(MFQSelf_c * ident_c) * Race [Latinx] |
0.24 | -0.06 – 0.54 | 0.119 |
|
(MFQSelf_c * ident_c) * Race [Caucasian] |
0.16 | -0.14 – 0.45 | 0.295 |
|
(MFQSelf_c * ident_c) * Race [Multiracial] |
0.13 | -0.19 – 0.44 | 0.434 |
|
(MFQSelf_c * ident_c) * Race [Other] |
0.17 | -0.18 – 0.53 | 0.344 |
|
(MFQStereo_c * ident_c) * Race [Asian] |
-0.15 | -0.40 – 0.10 | 0.231 |
|
(MFQStereo_c * ident_c) * Race [Black] |
-0.16 | -0.41 – 0.09 | 0.208 |
|
(MFQStereo_c * ident_c) * Race [Latinx] |
-0.05 | -0.30 – 0.20 | 0.708 |
|
(MFQStereo_c * ident_c) * Race [Caucasian] |
-0.13 | -0.37 – 0.12 | 0.318 |
|
(MFQStereo_c * ident_c) * Race [Multiracial] |
-0.10 | -0.35 – 0.15 | 0.438 |
|
(MFQStereo_c * ident_c) * Race [Other] |
-0.19 | -0.48 – 0.10 | 0.198 |
|
(inoutgroup_effects ident_c) Race [Asian] |
-0.10 | -0.54 – 0.33 | 0.643 |
|
(inoutgroup_effects ident_c) Race [Black] |
-0.26 | -0.73 – 0.20 | 0.270 |
|
(inoutgroup_effects ident_c) Race [Latinx] |
0.12 | -0.31 – 0.56 | 0.575 |
|
(inoutgroup_effects ident_c) Race [Caucasian] |
-0.08 | -0.42 – 0.27 | 0.661 |
|
(MFQSelf_c * MFQStereo_c * inoutgroup_effects) * ident_c |
-0.04 | -0.08 – 0.01 | 0.113 |
|
(MFQSelf_c * MFQStereo_c * inoutgroup_effects) * Race [Asian] |
0.19 | -0.07 – 0.45 | 0.159 |
|
(MFQSelf_c * MFQStereo_c * inoutgroup_effects) * Race [Black] |
0.19 | -0.07 – 0.45 | 0.146 |
|
(MFQSelf_c * MFQStereo_c * inoutgroup_effects) * Race [Latinx] |
0.19 | -0.06 – 0.45 | 0.138 |
|
(MFQSelf_c * MFQStereo_c * inoutgroup_effects) * Race [Caucasian] |
0.19 | -0.05 – 0.44 | 0.123 |
|
(MFQSelf_c * MFQStereo_c * inoutgroup_effects) * Race [Multiracial] |
0.06 | -0.21 – 0.33 | 0.659 |
|
(MFQSelf_c * MFQStereo_c * ident_c) * Race [Asian] |
0.00 | -0.12 – 0.12 | 0.996 |
|
(MFQSelf_c * MFQStereo_c * ident_c) * Race [Black] |
0.02 | -0.10 – 0.14 | 0.742 |
|
(MFQSelf_c * MFQStereo_c * ident_c) * Race [Latinx] |
0.04 | -0.07 – 0.16 | 0.455 |
|
(MFQSelf_c * MFQStereo_c * ident_c) * Race [Caucasian] |
0.03 | -0.09 – 0.14 | 0.656 |
|
(MFQSelf_c * MFQStereo_c * ident_c) * Race [Multiracial] |
0.02 | -0.10 – 0.14 | 0.798 |
|
(MFQSelf_c * MFQStereo_c * ident_c) * Race [Other] |
0.00 | -0.13 – 0.13 | 0.979 |
|
(MFQSelf_c inoutgroup_effects ident_c) * Race [Asian] |
-0.04 | -0.25 – 0.17 | 0.687 |
|
(MFQSelf_c inoutgroup_effects ident_c) * Race [Black] |
-0.02 | -0.25 – 0.22 | 0.898 |
|
(MFQSelf_c inoutgroup_effects ident_c) * Race [Latinx] |
-0.15 | -0.36 – 0.05 | 0.138 |
|
(MFQSelf_c inoutgroup_effects ident_c) * Race [Caucasian] |
-0.04 | -0.20 – 0.12 | 0.617 |
|
(MFQStereo_c inoutgroup_effects ident_c) * Race [Asian] |
0.13 | 0.01 – 0.24 | 0.028 |
|
(MFQStereo_c inoutgroup_effects ident_c) * Race [Black] |
-0.08 | -0.22 – 0.05 | 0.226 |
|
(MFQStereo_c inoutgroup_effects ident_c) * Race [Latinx] |
0.05 | -0.06 – 0.16 | 0.379 |
|
(MFQStereo_c inoutgroup_effects ident_c) * Race [Caucasian] |
0.03 | -0.07 – 0.12 | 0.588 |
|
(MFQSelf_c * MFQStereo_c * inoutgroup_effects ident_c) Race [Asian] |
0.11 | 0.05 – 0.18 | 0.001 |
|
(MFQSelf_c * MFQStereo_c * inoutgroup_effects ident_c) Race [Black] |
0.04 | -0.03 – 0.12 | 0.270 |
|
(MFQSelf_c * MFQStereo_c * inoutgroup_effects ident_c) Race [Latinx] |
0.05 | -0.00 – 0.11 | 0.073 |
|
(MFQSelf_c * MFQStereo_c * inoutgroup_effects ident_c) Race [Caucasian] |
0.03 | -0.01 – 0.08 | 0.170 |
| Random Effects | |||
| σ2 | 1.30 | ||
| τ00 subid | 0.46 | ||
| τ11 subid.MFQSelf_c | 0.09 | ||
| ρ01 subid | -0.02 | ||
| ICC | 0.33 | ||
| N subid | 670 | ||
| Observations | 19925 | ||
| Marginal R2 / Conditional R2 | 0.226 / 0.482 | ||
Model is rank deficient, not enough power to check if results differ by race. Could possibly do white vs minorities, but that doesn’t feel telling enough.
gender_analysis <- clean_data2 %>%
select(subid, MFQ_Number, MFQSelf_c, MFQStereo_c, MFQTarg, ident_c, PT_effects,
inoutgroup_effects, par_cand, Gender) %>%
unique() %>%
na.omit()
projmodel_stereo_gender <- lmer(MFQTarg ~ MFQSelf_c * MFQStereo_c *
inoutgroup_effects * ident_c * Gender +
(MFQSelf_c|subid),
data = gender_analysis)
tab_model(projmodel_stereo_gender,
title = "Projection moderated by stereotyping and gender")
| MFQ Targ | |||
|---|---|---|---|
| Predictors | Estimates | CI | p |
| (Intercept) | 4.03 | 3.94 – 4.12 | <0.001 |
| MFQSelf_c | 0.14 | 0.09 – 0.19 | <0.001 |
| MFQStereo_c | 0.04 | 0.01 – 0.07 | 0.003 |
| inoutgroup_effects | 0.30 | 0.11 – 0.48 | 0.002 |
| ident_c | 0.03 | -0.03 – 0.08 | 0.322 |
| Gender [Female] | 0.25 | 0.14 – 0.37 | <0.001 |
| Gender [Other] | 0.14 | -0.91 – 1.19 | 0.792 |
| MFQSelf_c * MFQStereo_c | -0.03 | -0.04 – -0.01 | <0.001 |
|
MFQSelf_c * inoutgroup_effects |
0.35 | 0.26 – 0.44 | <0.001 |
|
MFQStereo_c * inoutgroup_effects |
0.60 | 0.55 – 0.65 | <0.001 |
| MFQSelf_c * ident_c | 0.00 | -0.02 – 0.03 | 0.727 |
| MFQStereo_c * ident_c | -0.05 | -0.07 – -0.04 | <0.001 |
|
inoutgroup_effects * ident_c |
0.30 | 0.19 – 0.40 | <0.001 |
|
MFQSelf_c * Gender [Female] |
-0.00 | -0.06 – 0.05 | 0.954 |
|
MFQSelf_c * Gender [Other] |
0.08 | -0.41 – 0.57 | 0.756 |
|
MFQStereo_c * Gender [Female] |
0.05 | 0.02 – 0.09 | 0.002 |
|
MFQStereo_c * Gender [Other] |
0.04 | -0.31 – 0.40 | 0.824 |
|
inoutgroup_effects * Gender [Female] |
-0.10 | -0.32 – 0.13 | 0.407 |
|
inoutgroup_effects * Gender [Other] |
0.43 | -1.67 – 2.53 | 0.689 |
| ident_c * Gender [Female] | 0.02 | -0.04 – 0.09 | 0.493 |
| ident_c * Gender [Other] | -0.15 | -0.56 – 0.25 | 0.452 |
|
(MFQSelf_c * MFQStereo_c) * inoutgroup_effects |
0.02 | -0.01 – 0.05 | 0.190 |
|
(MFQSelf_c * MFQStereo_c) * ident_c |
-0.01 | -0.01 – 0.00 | 0.153 |
|
(MFQSelf_c inoutgroup_effects) ident_c |
0.03 | -0.02 – 0.08 | 0.253 |
|
(MFQStereo_c inoutgroup_effects) ident_c |
0.02 | -0.01 – 0.05 | 0.202 |
|
(MFQSelf_c * MFQStereo_c) * Gender [Female] |
0.00 | -0.02 – 0.02 | 0.964 |
|
(MFQSelf_c * MFQStereo_c) * Gender [Other] |
0.00 | -0.17 – 0.17 | 0.995 |
|
(MFQSelf_c inoutgroup_effects) Gender [Female] |
0.04 | -0.08 – 0.15 | 0.536 |
|
(MFQSelf_c inoutgroup_effects) Gender [Other] |
-0.45 | -1.43 – 0.53 | 0.366 |
|
(MFQStereo_c inoutgroup_effects) Gender [Female] |
-0.00 | -0.07 – 0.06 | 0.949 |
|
(MFQStereo_c inoutgroup_effects) Gender [Other] |
-0.23 | -0.94 – 0.48 | 0.530 |
|
(MFQSelf_c * ident_c) * Gender [Female] |
0.00 | -0.03 – 0.03 | 0.914 |
|
(MFQSelf_c * ident_c) * Gender [Other] |
-0.01 | -0.19 – 0.18 | 0.953 |
|
(MFQStereo_c * ident_c) * Gender [Female] |
0.03 | 0.01 – 0.05 | 0.001 |
|
(MFQStereo_c * ident_c) * Gender [Other] |
0.04 | -0.14 – 0.22 | 0.650 |
|
(inoutgroup_effects ident_c) Gender [Female] |
-0.09 | -0.22 – 0.04 | 0.163 |
|
(inoutgroup_effects ident_c) Gender [Other] |
0.12 | -0.68 – 0.93 | 0.768 |
|
(MFQSelf_c * MFQStereo_c * inoutgroup_effects) * ident_c |
-0.01 | -0.03 – 0.00 | 0.138 |
|
(MFQSelf_c * MFQStereo_c * inoutgroup_effects) * Gender [Female] |
-0.01 | -0.05 – 0.02 | 0.518 |
|
(MFQSelf_c * MFQStereo_c * inoutgroup_effects) * Gender [Other] |
-0.27 | -0.61 – 0.08 | 0.127 |
|
(MFQSelf_c * MFQStereo_c * ident_c) * Gender [Female] |
-0.00 | -0.01 – 0.01 | 0.766 |
|
(MFQSelf_c * MFQStereo_c * ident_c) * Gender [Other] |
0.01 | -0.08 – 0.09 | 0.900 |
|
(MFQSelf_c inoutgroup_effects ident_c) * Gender [Female] |
-0.03 | -0.10 – 0.03 | 0.282 |
|
(MFQSelf_c inoutgroup_effects ident_c) * Gender [Other] |
-0.29 | -0.66 – 0.09 | 0.132 |
|
(MFQStereo_c inoutgroup_effects ident_c) * Gender [Female] |
-0.02 | -0.06 – 0.02 | 0.261 |
|
(MFQStereo_c inoutgroup_effects ident_c) * Gender [Other] |
0.22 | -0.13 – 0.58 | 0.218 |
|
(MFQSelf_c * MFQStereo_c * inoutgroup_effects ident_c) Gender [Female] |
0.02 | -0.00 – 0.04 | 0.065 |
|
(MFQSelf_c * MFQStereo_c * inoutgroup_effects ident_c) Gender [Other] |
-0.03 | -0.20 – 0.14 | 0.754 |
| Random Effects | |||
| σ2 | 1.31 | ||
| τ00 subid | 0.44 | ||
| τ11 subid.MFQSelf_c | 0.08 | ||
| ρ01 subid | -0.04 | ||
| ICC | 0.32 | ||
| N subid | 673 | ||
| Observations | 20015 | ||
| Marginal R2 / Conditional R2 | 0.223 / 0.473 | ||
Gender does not moderate the results.
f1_gender <- lmer(MFQTarg ~ MFQSelf_c*Identification_c*Condition*Gender +
(MFQSelf_c|Subject),
data = pptnpf1_comb, na.action = na.omit)
When adding gender,the model failed to converge.
f1_race <- lmer(MFQTarg ~ MFQSelf_c*Identification_c*Condition*Race +
(MFQSelf_c|Subject),
data = pptnpf1_comb, na.action = na.omit)
When adding race, the fixed effect model was rank deficient and dropped predictors/comparisons.
npf2_clean <- npf2_clean %>%
mutate(gender = as.factor(gender),
race = as.factor(race))
f2_gender <- lmer(targ ~ self_c*ident_c*condition*gender +
(self_c|sub_id),
data = npf2_clean, na.action = na.omit)
Fixed effect model is rank deficient
f2_race <- lmer(targ ~ self_c*ident_c*condition*race +
(self_c|sub_id),
data = npf2_clean, na.action = na.omit)
Fixed effect model is rank deficient. We don’t have enough power to include extra categorical variables…
downstream_analysis <- clean_data2 %>%
select(subid, MFQ_Number, MFQSelf, MFQSelf_c, MFQStereo_c, MFQTarg, ident_c,
inoutgroup_effects, par_cand, Liking, WilltoDiscuss, Contentious, Treatment, Valid, MFQStereo) %>%
unique() %>%
na.omit() %>%
group_by(subid) %>%
#getting the difference scores
#decided to not mean center difference scores, since 0 is meaningful (no difference = 100% projection)
mutate(proj_diff = abs(mean(MFQTarg) - mean(MFQSelf)),
stereo_diff = abs(mean(MFQTarg)- mean(MFQStereo)))
mfq_binding_diff <- mfq_binding %>%
group_by(subid) %>%
mutate(proj_bind_diff = abs(mean(MFQTarg) - mean(MFQSelf)),
stereo_bind_diff = abs(mean(MFQTarg) - mean(MFQStereo)))
mfq_individual_diff <- mfq_individual %>%
group_by(subid) %>%
mutate(proj_indiv_diff = abs(mean(MFQTarg) - mean(MFQStereo)),
stereo_indiv_diff = abs(mean(MFQTarg) - mean(MFQStereo))) %>%
na.omit()
liking_overall <- lm(Liking ~ proj_diff*stereo_diff*inoutgroup_effects*ident_c, downstream_analysis)
summary(liking_overall)
##
## Call:
## lm(formula = Liking ~ proj_diff * stereo_diff * inoutgroup_effects *
## ident_c, data = downstream_analysis)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.4540 -0.8020 0.2304 0.7574 4.0435
##
## Coefficients:
## Estimate Std. Error t value
## (Intercept) 4.719954 0.021130 223.377
## proj_diff -0.190032 0.034956 -5.436
## stereo_diff -0.165177 0.035573 -4.643
## inoutgroup_effects 1.663988 0.042260 39.375
## ident_c 0.153919 0.011369 13.539
## proj_diff:stereo_diff 0.077122 0.045368 1.700
## proj_diff:inoutgroup_effects -0.123782 0.069911 -1.771
## stereo_diff:inoutgroup_effects 0.630144 0.071146 8.857
## proj_diff:ident_c -0.111098 0.015032 -7.391
## stereo_diff:ident_c 0.106644 0.018233 5.849
## inoutgroup_effects:ident_c 0.230630 0.022737 10.143
## proj_diff:stereo_diff:inoutgroup_effects -0.105041 0.090736 -1.158
## proj_diff:stereo_diff:ident_c -0.017069 0.014762 -1.156
## proj_diff:inoutgroup_effects:ident_c -0.021255 0.030064 -0.707
## stereo_diff:inoutgroup_effects:ident_c 0.165531 0.036467 4.539
## proj_diff:stereo_diff:inoutgroup_effects:ident_c 0.003271 0.029524 0.111
## Pr(>|t|)
## (Intercept) < 0.0000000000000002 ***
## proj_diff 0.000000055011895 ***
## stereo_diff 0.000003450972714 ***
## inoutgroup_effects < 0.0000000000000002 ***
## ident_c < 0.0000000000000002 ***
## proj_diff:stereo_diff 0.0892 .
## proj_diff:inoutgroup_effects 0.0766 .
## stereo_diff:inoutgroup_effects < 0.0000000000000002 ***
## proj_diff:ident_c 0.000000000000152 ***
## stereo_diff:ident_c 0.000000005027905 ***
## inoutgroup_effects:ident_c < 0.0000000000000002 ***
## proj_diff:stereo_diff:inoutgroup_effects 0.2470
## proj_diff:stereo_diff:ident_c 0.2476
## proj_diff:inoutgroup_effects:ident_c 0.4796
## stereo_diff:inoutgroup_effects:ident_c 0.000005678979911 ***
## proj_diff:stereo_diff:inoutgroup_effects:ident_c 0.9118
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.262 on 19882 degrees of freedom
## Multiple R-squared: 0.4223, Adjusted R-squared: 0.4219
## F-statistic: 969.1 on 15 and 19882 DF, p-value: < 0.00000000000000022
#main effect of projection
ggplot(downstream_analysis, aes(proj_diff, Liking)) +
geom_smooth(method = "lm") +
theme_minimal() +
labs(title = "Liking predicted by projection of moral values",
subtitle = "Projection is the absolute value of the difference score \nbetween average MFQ scores for target and self",
x = "Projection (Higher numbers indicate lower projection)",
y = "Liking")
Counter projection can be seen as anything with a distance over 1, as that means that people are not listing their score as the same as the targets. The greater the distance demonstrates the greater the counter projection. As can be seen, as people counter project more, they report liking the target less. People who project normally report liking the target the most.
#interaction between projection and degree of identification
describe(downstream_analysis$ident_c)
## vars n mean sd median trimmed mad min max range skew kurtosis
## X1 1 19898 -0.03 1.78 -0.01 -0.04 1.8 -4.23 3.77 8 -0.02 -0.32
## se
## X1 0.01
liking_overall_2way <- effect(term = "proj_diff:ident_c",
xlevels = list(ident_c = c(-1.78, 0, 1.78)),
mod = liking_overall)
liking_overall_2way <- as.data.frame(liking_overall_2way)
liking_overall_2way$Identification <- as.factor(liking_overall_2way$ident_c)
ggplot(liking_overall_2way, aes(proj_diff, fit, group = Identification, color = Identification)) +
geom_smooth(method = "lm") +
theme_minimal() +
scale_color_discrete("Identification", breaks = c(-1.78, 0, 1.78),
labels = c("Weak identification \n (1 SD below)",
"Average identification",
"High identification \n (1 SD above)")) +
labs(title = "Liking predicted by projection of moral values",
subtitle = "Projection is the absolute value of the difference score between average \nMFQ scores for target and self. Higher scores = greater counter projection.",
x = "Projection (Higher = more counter projection)",
y = "Liking")
Those who weakly identify continue to like the target, even as the distance between their scores on the MFQ and their target’s increase (becoming less similar, less projection). As in-group identification increases, self-reported liking of the target decreases as counter projection increases.
liking_bind <- lm(Liking ~ proj_bind_diff*stereo_bind_diff*inoutgroup_effects*ident_c, mfq_binding_diff)
summary(liking_bind)
##
## Call:
## lm(formula = Liking ~ proj_bind_diff * stereo_bind_diff * inoutgroup_effects *
## ident_c, data = mfq_binding_diff)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.5388 -0.7824 0.1056 0.7848 3.5943
##
## Coefficients:
## Estimate Std. Error
## (Intercept) 4.794162 0.026896
## proj_bind_diff -0.090386 0.029169
## stereo_bind_diff 0.028208 0.030557
## inoutgroup_effects 1.464714 0.053792
## ident_c 0.089831 0.016064
## proj_bind_diff:stereo_bind_diff -0.044362 0.026371
## proj_bind_diff:inoutgroup_effects 0.139702 0.058339
## stereo_bind_diff:inoutgroup_effects -0.042776 0.061114
## proj_bind_diff:ident_c -0.004495 0.013961
## stereo_bind_diff:ident_c 0.115828 0.017389
## inoutgroup_effects:ident_c 0.190607 0.032128
## proj_bind_diff:stereo_bind_diff:inoutgroup_effects 0.112589 0.052742
## proj_bind_diff:stereo_bind_diff:ident_c -0.038439 0.011566
## proj_bind_diff:inoutgroup_effects:ident_c 0.124375 0.027921
## stereo_bind_diff:inoutgroup_effects:ident_c 0.129997 0.034779
## proj_bind_diff:stereo_bind_diff:inoutgroup_effects:ident_c -0.064567 0.023133
## t value
## (Intercept) 178.249
## proj_bind_diff -3.099
## stereo_bind_diff 0.923
## inoutgroup_effects 27.229
## ident_c 5.592
## proj_bind_diff:stereo_bind_diff -1.682
## proj_bind_diff:inoutgroup_effects 2.395
## stereo_bind_diff:inoutgroup_effects -0.700
## proj_bind_diff:ident_c -0.322
## stereo_bind_diff:ident_c 6.661
## inoutgroup_effects:ident_c 5.933
## proj_bind_diff:stereo_bind_diff:inoutgroup_effects 2.135
## proj_bind_diff:stereo_bind_diff:ident_c -3.323
## proj_bind_diff:inoutgroup_effects:ident_c 4.455
## stereo_bind_diff:inoutgroup_effects:ident_c 3.738
## proj_bind_diff:stereo_bind_diff:inoutgroup_effects:ident_c -2.791
## Pr(>|t|)
## (Intercept) < 0.0000000000000002
## proj_bind_diff 0.001949
## stereo_bind_diff 0.355960
## inoutgroup_effects < 0.0000000000000002
## ident_c 0.0000000229220
## proj_bind_diff:stereo_bind_diff 0.092554
## proj_bind_diff:inoutgroup_effects 0.016651
## stereo_bind_diff:inoutgroup_effects 0.483980
## proj_bind_diff:ident_c 0.747462
## stereo_bind_diff:ident_c 0.0000000000284
## inoutgroup_effects:ident_c 0.0000000030606
## proj_bind_diff:stereo_bind_diff:inoutgroup_effects 0.032805
## proj_bind_diff:stereo_bind_diff:ident_c 0.000892
## proj_bind_diff:inoutgroup_effects:ident_c 0.0000084843620
## stereo_bind_diff:inoutgroup_effects:ident_c 0.000186
## proj_bind_diff:stereo_bind_diff:inoutgroup_effects:ident_c 0.005261
##
## (Intercept) ***
## proj_bind_diff **
## stereo_bind_diff
## inoutgroup_effects ***
## ident_c ***
## proj_bind_diff:stereo_bind_diff .
## proj_bind_diff:inoutgroup_effects *
## stereo_bind_diff:inoutgroup_effects
## proj_bind_diff:ident_c
## stereo_bind_diff:ident_c ***
## inoutgroup_effects:ident_c ***
## proj_bind_diff:stereo_bind_diff:inoutgroup_effects *
## proj_bind_diff:stereo_bind_diff:ident_c ***
## proj_bind_diff:inoutgroup_effects:ident_c ***
## stereo_bind_diff:inoutgroup_effects:ident_c ***
## proj_bind_diff:stereo_bind_diff:inoutgroup_effects:ident_c **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.215 on 11926 degrees of freedom
## Multiple R-squared: 0.4644, Adjusted R-squared: 0.4637
## F-statistic: 689.3 on 15 and 11926 DF, p-value: < 0.00000000000000022
#main effect of projection
ggplot(mfq_binding_diff, aes(proj_bind_diff, Liking)) +
geom_smooth(method = "lm") +
theme_minimal() +
labs(title = "Liking predicted by projection of binding morals",
subtitle = "Projection is the absolute value of the difference score \nbetween average MFQ scores for target and self",
x = "Projection (Higher numbers indicate lower projection)",
y = "Liking")
Same as overall plot.
liking_bind_2way_group <- effect(term = "proj_bind_diff:inoutgroup_effects",
xlevels = list(inoutgroup_effects = c(-.5,.5)),
mod = liking_bind)
liking_bind_2way_group <- as.data.frame(liking_bind_2way_group)
liking_bind_2way_group$group <- as.factor(liking_bind_2way_group$inoutgroup_effects)
ggplot(liking_bind_2way_group, aes(proj_bind_diff, fit, group = group, color = group)) +
geom_smooth(method = "lm", se = FALSE, size = .7, colour = "black",
aes(linetype = group)) +
theme_minimal() +
scale_linetype_manual("Group", breaks = c(-.5, .5),
labels = c("Out-group", "In-group"),
values = c("solid",
"dashed")) +
labs(title = "Liking predicted by projection of binding moral values",
subtitle = "Projection is the absolute value of the difference score between average \nMFQ scores for target and self. Higher scores = greater counter projection.",
x = "Projection (Higher = more counter projection)",
y = "Liking")
Liking stays high with the in-group, regardless of their degree of projection. Liking is highest for the out-group when they are projecting. As counter projection increases (the distance becomes further), liking decreases for the out-group.
describe(stereo_analysis$ident_c)
## vars n mean sd median trimmed mad min max range skew kurtosis
## X1 1 20045 -0.03 1.78 -0.01 -0.04 1.8 -4.23 3.77 8 -0.02 -0.32
## se
## X1 0.01
binding_liking_3way <- effect(term = "proj_bind_diff:inoutgroup_effects:ident_c",
xlevels = list(ident_c = c(-1.78, 0, 1.78),
inoutgroup_effects = c(-.5,.5)),
mod = liking_bind)
binding_liking_3way <- as.data.frame(binding_liking_3way)
binding_liking_3way$Group <- as.factor(binding_liking_3way$inoutgroup_effects)
binding_liking_3way$Identification <- as.factor(binding_liking_3way$ident_c)
ident_label <- c("-1.78" = "Low identification \n (1 SD below)",
"0" = "Average identification",
"1.78" = "High identificatiion \n(1 SD above)")
ggplot(binding_liking_3way, aes(proj_bind_diff,
fit,
group = Group,
color = Group)) +
geom_smooth(method = "lm", se = FALSE, size = .7, colour = "black",
aes(linetype = Group)) +
facet_wrap(~Identification, labeller =
labeller(Identification = ident_label)) +
scale_linetype_manual("Group", breaks = c(-.5, .5),
labels = c("Out-group", "In-group"),
values = c("solid",
"dashed")) +
theme_minimal() +
labs(title = "Liking predicted by projection of binding morals, \nin-group identification and group membership",
subtitle = "Controlling for stereotyping",
x = "Projection difference scores (higher = more counter projection)",
y = "Liking")
As can be seen in the plot above, people report liking an in-group target at a similiar rate regardless of projection, while liking of the out-group target decreases as both counter projection and in-group identification increase. Thus, the people who report liking the least are considering an out-group target and are highly identified with their in-group as well as projecting their own morals the least.
liking_indiv <- lm(Liking ~ proj_indiv_diff*stereo_indiv_diff*inoutgroup_effects*ident_c, mfq_individual_diff)
summary(liking_indiv)
##
## Call:
## lm(formula = Liking ~ proj_indiv_diff * stereo_indiv_diff * inoutgroup_effects *
## ident_c, data = mfq_individual_diff)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.5668 -0.7639 0.1056 0.7996 3.6753
##
## Coefficients: (4 not defined because of singularities)
## Estimate
## (Intercept) 4.87853
## proj_indiv_diff -0.17945
## stereo_indiv_diff NA
## inoutgroup_effects 1.28560
## ident_c 0.04418
## proj_indiv_diff:stereo_indiv_diff -0.08643
## proj_indiv_diff:inoutgroup_effects 0.62506
## stereo_indiv_diff:inoutgroup_effects NA
## proj_indiv_diff:ident_c 0.25313
## stereo_indiv_diff:ident_c NA
## inoutgroup_effects:ident_c 0.17232
## proj_indiv_diff:stereo_indiv_diff:inoutgroup_effects -0.12613
## proj_indiv_diff:stereo_indiv_diff:ident_c -0.10738
## proj_indiv_diff:inoutgroup_effects:ident_c 0.47335
## stereo_indiv_diff:inoutgroup_effects:ident_c NA
## proj_indiv_diff:stereo_indiv_diff:inoutgroup_effects:ident_c -0.20190
## Std. Error t value
## (Intercept) 0.03012 161.976
## proj_indiv_diff 0.06826 -2.629
## stereo_indiv_diff NA NA
## inoutgroup_effects 0.06024 21.342
## ident_c 0.01492 2.960
## proj_indiv_diff:stereo_indiv_diff 0.03666 -2.358
## proj_indiv_diff:inoutgroup_effects 0.13653 4.578
## stereo_indiv_diff:inoutgroup_effects NA NA
## proj_indiv_diff:ident_c 0.03434 7.372
## stereo_indiv_diff:ident_c NA NA
## inoutgroup_effects:ident_c 0.02985 5.773
## proj_indiv_diff:stereo_indiv_diff:inoutgroup_effects 0.07331 -1.720
## proj_indiv_diff:stereo_indiv_diff:ident_c 0.01721 -6.238
## proj_indiv_diff:inoutgroup_effects:ident_c 0.06868 6.892
## stereo_indiv_diff:inoutgroup_effects:ident_c NA NA
## proj_indiv_diff:stereo_indiv_diff:inoutgroup_effects:ident_c 0.03443 -5.865
## Pr(>|t|)
## (Intercept) < 0.0000000000000002
## proj_indiv_diff 0.00859
## stereo_indiv_diff NA
## inoutgroup_effects < 0.0000000000000002
## ident_c 0.00308
## proj_indiv_diff:stereo_indiv_diff 0.01841
## proj_indiv_diff:inoutgroup_effects 0.000004761326363
## stereo_indiv_diff:inoutgroup_effects NA
## proj_indiv_diff:ident_c 0.000000000000185
## stereo_indiv_diff:ident_c NA
## inoutgroup_effects:ident_c 0.000000008089039
## proj_indiv_diff:stereo_indiv_diff:inoutgroup_effects 0.08539
## proj_indiv_diff:stereo_indiv_diff:ident_c 0.000000000465508
## proj_indiv_diff:inoutgroup_effects:ident_c 0.000000000005905
## stereo_indiv_diff:inoutgroup_effects:ident_c NA
## proj_indiv_diff:stereo_indiv_diff:inoutgroup_effects:ident_c 0.000000004673593
##
## (Intercept) ***
## proj_indiv_diff **
## stereo_indiv_diff
## inoutgroup_effects ***
## ident_c **
## proj_indiv_diff:stereo_indiv_diff *
## proj_indiv_diff:inoutgroup_effects ***
## stereo_indiv_diff:inoutgroup_effects
## proj_indiv_diff:ident_c ***
## stereo_indiv_diff:ident_c
## inoutgroup_effects:ident_c ***
## proj_indiv_diff:stereo_indiv_diff:inoutgroup_effects .
## proj_indiv_diff:stereo_indiv_diff:ident_c ***
## proj_indiv_diff:inoutgroup_effects:ident_c ***
## stereo_indiv_diff:inoutgroup_effects:ident_c
## proj_indiv_diff:stereo_indiv_diff:inoutgroup_effects:ident_c ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.206 on 7944 degrees of freedom
## Multiple R-squared: 0.4728, Adjusted R-squared: 0.4721
## F-statistic: 647.8 on 11 and 7944 DF, p-value: < 0.00000000000000022
linear_check <- lm(Liking ~ proj_indiv_diff*stereo_indiv_diff, mfq_individual_diff)
summary(linear_check)
##
## Call:
## lm(formula = Liking ~ proj_indiv_diff * stereo_indiv_diff, data = mfq_individual_diff)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.3951 -0.9955 0.0045 1.0045 3.8465
##
## Coefficients: (1 not defined because of singularities)
## Estimate Std. Error t value
## (Intercept) 5.395091 0.031230 172.753
## proj_indiv_diff -0.961255 0.053442 -17.987
## stereo_indiv_diff NA NA NA
## proj_indiv_diff:stereo_indiv_diff 0.005448 0.015889 0.343
## Pr(>|t|)
## (Intercept) <0.0000000000000002 ***
## proj_indiv_diff <0.0000000000000002 ***
## stereo_indiv_diff NA
## proj_indiv_diff:stereo_indiv_diff 0.732
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.439 on 7953 degrees of freedom
## Multiple R-squared: 0.2486, Adjusted R-squared: 0.2484
## F-statistic: 1315 on 2 and 7953 DF, p-value: < 0.00000000000000022
Getting NA’s for estimates for stereotyping for individuating morals - a search online says this is because it is linearly dependent on one of the other variables (has reached singularity). After running models checking interactions between each variable and stereo_indiv_diff, it is clear for individuating morals, stereotyping and projection are linearly related (pretty much the same). This is not the case for binding morals or when the morals are combined in the overall model.
Decided not to plot individuating models, as we cannot claim this is due to counter projection, as projection is the same as stereotyping in this case.
discuss_overall <- lm(WilltoDiscuss ~ proj_diff*stereo_diff*inoutgroup_effects*ident_c, downstream_analysis)
summary(discuss_overall)
##
## Call:
## lm(formula = WilltoDiscuss ~ proj_diff * stereo_diff * inoutgroup_effects *
## ident_c, data = downstream_analysis)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.0428 -1.0559 0.2463 1.0248 3.3346
##
## Coefficients:
## Estimate Std. Error t value
## (Intercept) 5.09481 0.02811 181.226
## proj_diff -0.18734 0.04651 -4.028
## stereo_diff -0.33715 0.04733 -7.123
## inoutgroup_effects 2.15681 0.05623 38.360
## ident_c -0.07428 0.01513 -4.911
## proj_diff:stereo_diff 0.17127 0.06036 2.837
## proj_diff:inoutgroup_effects -0.75887 0.09302 -8.159
## stereo_diff:inoutgroup_effects -0.62032 0.09466 -6.553
## proj_diff:ident_c 0.05186 0.02000 2.593
## stereo_diff:ident_c 0.36063 0.02426 14.866
## inoutgroup_effects:ident_c 0.22031 0.03025 7.283
## proj_diff:stereo_diff:inoutgroup_effects 0.56341 0.12072 4.667
## proj_diff:stereo_diff:ident_c -0.11896 0.01964 -6.057
## proj_diff:inoutgroup_effects:ident_c -0.08602 0.04000 -2.151
## stereo_diff:inoutgroup_effects:ident_c 0.40581 0.04852 8.364
## proj_diff:stereo_diff:inoutgroup_effects:ident_c -0.14472 0.03928 -3.684
## Pr(>|t|)
## (Intercept) < 0.0000000000000002 ***
## proj_diff 0.000056402922172085 ***
## stereo_diff 0.000000000001088290 ***
## inoutgroup_effects < 0.0000000000000002 ***
## ident_c 0.000000913693464514 ***
## proj_diff:stereo_diff 0.00455 **
## proj_diff:inoutgroup_effects 0.000000000000000359 ***
## stereo_diff:inoutgroup_effects 0.000000000057695994 ***
## proj_diff:ident_c 0.00952 **
## stereo_diff:ident_c < 0.0000000000000002 ***
## inoutgroup_effects:ident_c 0.000000000000339752 ***
## proj_diff:stereo_diff:inoutgroup_effects 0.000003076300964101 ***
## proj_diff:stereo_diff:ident_c 0.000000001412524968 ***
## proj_diff:inoutgroup_effects:ident_c 0.03153 *
## stereo_diff:inoutgroup_effects:ident_c < 0.0000000000000002 ***
## proj_diff:stereo_diff:inoutgroup_effects:ident_c 0.00023 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.679 on 19882 degrees of freedom
## Multiple R-squared: 0.2349, Adjusted R-squared: 0.2343
## F-statistic: 406.9 on 15 and 19882 DF, p-value: < 0.00000000000000022
#main effect of projection
ggplot(downstream_analysis, aes(proj_diff, WilltoDiscuss)) +
geom_smooth(method = "lm") +
theme_minimal() +
labs(title = "Willingness to have a coversation with target predicted by projection of morals",
subtitle = "Projection is the absolute value of the difference score \nbetween average MFQ scores for target and self",
x = "Projection (Higher numbers indicate lower projection)",
y = "Willingness")
As counter projection increased, willingness to have a conversation with the target decreased.
discuss_overall_2way_group <- effect(term = "proj_diff:inoutgroup_effects",
xlevels = list(inoutgroup_effects = c(-.5,.5)),
mod = discuss_overall)
discuss_overall_2way_group <- as.data.frame(discuss_overall_2way_group)
discuss_overall_2way_group$group <- as.factor(discuss_overall_2way_group$inoutgroup_effects)
ggplot(discuss_overall_2way_group, aes(proj_diff, fit, group = group, color = group)) +
geom_smooth(method = "lm", se = FALSE, size = .7, colour = "black",
aes(linetype = group)) +
theme_minimal() +
scale_linetype_manual("Group", breaks = c(-.5, .5),
labels = c("Out-group", "In-group"),
values = c("solid",
"dashed")) +
labs(title = "Willingness to have conversation with target predicted by projection and target group",
subtitle = "Projection is the absolute value of the difference score between average \nMFQ scores for target and self. Higher scores = greater counter projection.",
x = "Projection (Higher = more counter projection)",
y = "Willingness")
Willingness to have a conversation increases for people in the out-group as counter projection increases, while it decreases for the in-group as counter projection occurs. One possible explanation may be that people are willing to have a conversation with their out-group who they see as morally different in order to change them, while people in their in-group who have opposite morals might be more difficult (since they seemingly chose not to have some of the morals the in-group generally seems to share).
discuss_overall_2way_ident <- effect(term = "proj_diff:ident_c",
xlevels = list(ident_c = c(-1.78, 0, 1.78)),
mod = discuss_overall)
discuss_overall_2way_ident <- as.data.frame(discuss_overall_2way_ident)
discuss_overall_2way_ident$Identification <- as.factor(discuss_overall_2way_ident$ident_c)
ggplot(discuss_overall_2way_ident, aes(proj_diff, fit, group = Identification, color = Identification)) +
geom_smooth(method = "lm", se = FALSE, size = .7, colour = "black",
aes(linetype = Identification)) +
theme_minimal() +
scale_linetype_manual("Identification", breaks = c(-1.78, 0, 1.78),
labels = c("Weak (1 SD below)",
"Average",
"High (1 SD above)"),
values = c("solid",
"dashed",
"dotted")) +
labs(title = "Willingness to have conversation with target predicted by projection and identification",
subtitle = "Projection is the absolute value of the difference score between average \nMFQ scores for target and self. Higher scores = greater counter projection.",
x = "Projection (Higher = more counter projection)",
y = "Willingness")
Willingness to have a conversation starts highest for all levels of identification when they project and this decreases as counter projection increases. However, as can be seen in the plot, the decrease in willingness to discuss as counter projection increases is steepest in higher identifiers, followed by average identifiers.
discuss_overall_3way <- effect(term = "proj_diff:inoutgroup_effects:ident_c",
xlevels = list(ident_c = c(-1.78, 0, 1.78),
inoutgroup_effects = c(-.5,.5)),
mod = discuss_overall)
discuss_overall_3way<- as.data.frame(discuss_overall_3way)
discuss_overall_3way$Group <- as.factor(discuss_overall_3way$inoutgroup_effects)
discuss_overall_3way$Identification <- as.factor(discuss_overall_3way$ident_c)
ident_label <- c("-1.78" = "Low identification \n (1 SD below)",
"0" = "Average identification",
"1.78" = "High identificatiion \n(1 SD above)")
ggplot(discuss_overall_3way, aes(proj_diff,
fit,
group = Group,
color = Group)) +
geom_smooth(method = "lm", se = FALSE, size = .7, colour = "black",
aes(linetype = Group)) +
facet_wrap(~Identification, labeller =
labeller(Identification = ident_label)) +
scale_linetype_manual("Group", breaks = c(-.5, .5),
labels = c("Out-group", "In-group"),
values = c("solid",
"dashed")) +
theme_minimal() +
labs(title = "Willingness to discuss predicted by projection of morals, \nin-group identification and group membership",
subtitle = "Controlling for stereotyping",
x = "Projection difference scores (higher = more counter projection)",
y = "Willingness")
Willingness to have a conversation increases the most for out-group targets as counter projection increases and in-group identification increases (weird…). In contrast, willingness to have a conversation with in-group targets decrease as both in-group identification and counter projection increase.
discuss_bind <- lm(WilltoDiscuss ~ proj_bind_diff*stereo_bind_diff*inoutgroup_effects*ident_c, mfq_binding_diff)
summary(discuss_bind)
##
## Call:
## lm(formula = WilltoDiscuss ~ proj_bind_diff * stereo_bind_diff *
## inoutgroup_effects * ident_c, data = mfq_binding_diff)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.8811 -1.0349 0.2295 1.0608 3.3868
##
## Coefficients:
## Estimate Std. Error
## (Intercept) 4.992709 0.037290
## proj_bind_diff -0.194038 0.040442
## stereo_bind_diff 0.016543 0.042366
## inoutgroup_effects 2.121271 0.074579
## ident_c 0.009517 0.022272
## proj_bind_diff:stereo_bind_diff 0.003772 0.036562
## proj_bind_diff:inoutgroup_effects -0.241324 0.080884
## stereo_bind_diff:inoutgroup_effects -0.641452 0.084732
## proj_bind_diff:ident_c 0.059338 0.019355
## stereo_bind_diff:ident_c 0.119775 0.024109
## inoutgroup_effects:ident_c 0.027979 0.044543
## proj_bind_diff:stereo_bind_diff:inoutgroup_effects 0.188985 0.073124
## proj_bind_diff:stereo_bind_diff:ident_c -0.049685 0.016036
## proj_bind_diff:inoutgroup_effects:ident_c 0.212977 0.038711
## stereo_bind_diff:inoutgroup_effects:ident_c 0.310620 0.048219
## proj_bind_diff:stereo_bind_diff:inoutgroup_effects:ident_c -0.150533 0.032072
## t value
## (Intercept) 133.890
## proj_bind_diff -4.798
## stereo_bind_diff 0.390
## inoutgroup_effects 28.443
## ident_c 0.427
## proj_bind_diff:stereo_bind_diff 0.103
## proj_bind_diff:inoutgroup_effects -2.984
## stereo_bind_diff:inoutgroup_effects -7.570
## proj_bind_diff:ident_c 3.066
## stereo_bind_diff:ident_c 4.968
## inoutgroup_effects:ident_c 0.628
## proj_bind_diff:stereo_bind_diff:inoutgroup_effects 2.584
## proj_bind_diff:stereo_bind_diff:ident_c -3.098
## proj_bind_diff:inoutgroup_effects:ident_c 5.502
## stereo_bind_diff:inoutgroup_effects:ident_c 6.442
## proj_bind_diff:stereo_bind_diff:inoutgroup_effects:ident_c -4.694
## Pr(>|t|)
## (Intercept) < 0.0000000000000002
## proj_bind_diff 0.00000162237218
## stereo_bind_diff 0.69619
## inoutgroup_effects < 0.0000000000000002
## ident_c 0.66914
## proj_bind_diff:stereo_bind_diff 0.91784
## proj_bind_diff:inoutgroup_effects 0.00285
## stereo_bind_diff:inoutgroup_effects 0.00000000000004
## proj_bind_diff:ident_c 0.00218
## stereo_bind_diff:ident_c 0.00000068586026
## inoutgroup_effects:ident_c 0.52992
## proj_bind_diff:stereo_bind_diff:inoutgroup_effects 0.00977
## proj_bind_diff:stereo_bind_diff:ident_c 0.00195
## proj_bind_diff:inoutgroup_effects:ident_c 0.00000003838439
## stereo_bind_diff:inoutgroup_effects:ident_c 0.00000000012253
## proj_bind_diff:stereo_bind_diff:inoutgroup_effects:ident_c 0.00000271482256
##
## (Intercept) ***
## proj_bind_diff ***
## stereo_bind_diff
## inoutgroup_effects ***
## ident_c
## proj_bind_diff:stereo_bind_diff
## proj_bind_diff:inoutgroup_effects **
## stereo_bind_diff:inoutgroup_effects ***
## proj_bind_diff:ident_c **
## stereo_bind_diff:ident_c ***
## inoutgroup_effects:ident_c
## proj_bind_diff:stereo_bind_diff:inoutgroup_effects **
## proj_bind_diff:stereo_bind_diff:ident_c **
## proj_bind_diff:inoutgroup_effects:ident_c ***
## stereo_bind_diff:inoutgroup_effects:ident_c ***
## proj_bind_diff:stereo_bind_diff:inoutgroup_effects:ident_c ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.685 on 11926 degrees of freedom
## Multiple R-squared: 0.2297, Adjusted R-squared: 0.2287
## F-statistic: 237 on 15 and 11926 DF, p-value: < 0.00000000000000022
#main effect of projection
ggplot(mfq_binding_diff, aes(proj_bind_diff, WilltoDiscuss)) +
geom_smooth(method = "lm") +
theme_minimal() +
labs(title = "Willingness to have a coversation with target predicted by projection of binding morals",
subtitle = "Projection is the absolute value of the difference score \nbetween average MFQ scores for target and self",
x = "Projection (Higher numbers indicate lower projection)",
y = "Willingness")
discuss_bind_2way_group <- effect(term = "proj_bind_diff:inoutgroup_effects",
xlevels = list(inoutgroup_effects = c(-.5,.5)),
mod = discuss_bind)
discuss_bind_2way_group <- as.data.frame(discuss_bind_2way_group)
discuss_bind_2way_group$group <- as.factor(discuss_bind_2way_group$inoutgroup_effects)
ggplot(discuss_bind_2way_group, aes(proj_bind_diff, fit, group = group, color = group)) +
geom_smooth(method = "lm", se = FALSE, size = .7, colour = "black",
aes(linetype = group)) +
theme_minimal() +
scale_linetype_manual("Group", breaks = c(-.5, .5),
labels = c("Out-group", "In-group"),
values = c("solid",
"dashed")) +
labs(title = "Willingness to have conversation with target predicted by projection of binding morals and target group",
subtitle = "Projection is the absolute value of the difference score between average \nMFQ scores for target and self. Higher scores = greater counter projection.",
x = "Projection (Higher = more counter projection)",
y = "Willingness")
Doesn’t look like much of an interaction… but, I guess, eventually they would overlap as counter projection increases… Either way, people start out less willing to have a conversation with their out-group than their in-group. As counter projection increases, people’s willingness to discuss decreases for both in and out-group targets, but more quickly for in-group targets.
discuss_bind_2way_ident <- effect(term = "proj_bind_diff:ident_c",
xlevels = list(ident_c = c(-1.78, 0, 1.78)),
mod = discuss_bind)
discuss_bind_2way_ident <- as.data.frame(discuss_bind_2way_ident)
discuss_bind_2way_ident$Identification <- as.factor(discuss_bind_2way_ident$ident_c)
ggplot(discuss_bind_2way_ident, aes(proj_bind_diff, fit, group = Identification, color = Identification)) +
geom_smooth(method = "lm", se = FALSE, size = .7, colour = "black",
aes(linetype = Identification)) +
theme_minimal() +
scale_linetype_manual("Identification", breaks = c(-1.78, 0, 1.78),
labels = c("Weak (1 SD below)",
"Average",
"High (1 SD above)"),
values = c("solid",
"dashed",
"dotted")) +
labs(title = "Willingness to have conversation with target predicted by projection of binding morals and identification",
subtitle = "Projection is the absolute value of the difference score between average \nMFQ scores for target and self. Higher scores = greater counter projection.",
x = "Projection (Higher = more counter projection)",
y = "Willingness")
Similar to the last model in not being much of an interaction…
discuss_bind_3way <- effect(term = "proj_bind_diff:inoutgroup_effects:ident_c",
xlevels = list(ident_c = c(-1.78, 0, 1.78),
inoutgroup_effects = c(-.5,.5)),
mod = discuss_bind)
discuss_bind_3way <- as.data.frame(discuss_bind_3way)
discuss_bind_3way$Group <- as.factor(discuss_bind_3way$inoutgroup_effects)
discuss_bind_3way$Identification <- as.factor(discuss_bind_3way$ident_c)
ident_label <- c("-1.78" = "Low identification \n (1 SD below)",
"0" = "Average identification",
"1.78" = "High identificatiion \n(1 SD above)")
ggplot(discuss_bind_3way, aes(proj_bind_diff,
fit,
group = Group,
color = Group)) +
geom_smooth(method = "lm", se = FALSE, size = .7, colour = "black",
aes(linetype = Group)) +
facet_wrap(~Identification, labeller =
labeller(Identification = ident_label)) +
scale_linetype_manual("Group", breaks = c(-.5, .5),
labels = c("Out-group", "In-group"),
values = c("solid",
"dashed")) +
theme_minimal() +
labs(title = "Willingness to discuss predicted by projection of binding morals, \nin-group identification and group membership",
subtitle = "Controlling for stereotyping",
x = "Projection difference scores (higher = more counter projection)",
y = "Willingness")
Now this is more interesting! People who are highly identified with their in-group report the highest willingness to have a conversation with an in-group target, especially when they are projecting. Their decrease in willingness to discuss with their out-group as counter projection increases is the least steep, still resulting in a higher willingness than all other conditions. The lowest willingness to have a conversation occurred with out-group targets when participants’ highly identified with their in-group and were counter projecting. Scores for willingness to discuss for in and out-group targets were the closet to one another when participants were weakly identified with their in-group and were counter projecting.
discuss_indiv <- lm(WilltoDiscuss ~ proj_indiv_diff*stereo_indiv_diff*ident_c, mfq_individual_diff)
summary(discuss_indiv)
##
## Call:
## lm(formula = WilltoDiscuss ~ proj_indiv_diff * stereo_indiv_diff *
## ident_c, data = mfq_individual_diff)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.7927 -1.3206 0.3249 1.4753 3.8827
##
## Coefficients: (2 not defined because of singularities)
## Estimate Std. Error t value
## (Intercept) 5.408883 0.040091 134.915
## proj_indiv_diff -0.505269 0.069415 -7.279
## stereo_indiv_diff NA NA NA
## ident_c 0.141374 0.020390 6.934
## proj_indiv_diff:stereo_indiv_diff -0.033770 0.020897 -1.616
## proj_indiv_diff:ident_c -0.024195 0.035072 -0.690
## stereo_indiv_diff:ident_c NA NA NA
## proj_indiv_diff:stereo_indiv_diff:ident_c -0.002586 0.010005 -0.258
## Pr(>|t|)
## (Intercept) < 0.0000000000000002 ***
## proj_indiv_diff 0.000000000000369 ***
## stereo_indiv_diff NA
## ident_c 0.000000000004424 ***
## proj_indiv_diff:stereo_indiv_diff 0.106
## proj_indiv_diff:ident_c 0.490
## stereo_indiv_diff:ident_c NA
## proj_indiv_diff:stereo_indiv_diff:ident_c 0.796
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.828 on 7950 degrees of freedom
## Multiple R-squared: 0.09315, Adjusted R-squared: 0.09258
## F-statistic: 163.3 on 5 and 7950 DF, p-value: < 0.00000000000000022
content_overall <- lm(Contentious ~ proj_diff*stereo_diff*inoutgroup_effects*ident_c, downstream_analysis)
summary(content_overall)
##
## Call:
## lm(formula = Contentious ~ proj_diff * stereo_diff * inoutgroup_effects *
## ident_c, data = downstream_analysis)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.930 -1.654 0.204 1.354 4.152
##
## Coefficients:
## Estimate Std. Error t value
## (Intercept) 3.75059 0.03014 124.445
## proj_diff 0.32046 0.04986 6.427
## stereo_diff 0.43910 0.05074 8.654
## inoutgroup_effects -1.80952 0.06028 -30.020
## ident_c -0.02977 0.01622 -1.836
## proj_diff:stereo_diff -0.21887 0.06471 -3.382
## proj_diff:inoutgroup_effects 0.44860 0.09972 4.499
## stereo_diff:inoutgroup_effects 0.76520 0.10148 7.540
## proj_diff:ident_c 0.11142 0.02144 5.197
## stereo_diff:ident_c 0.18932 0.02601 7.279
## inoutgroup_effects:ident_c -0.09929 0.03243 -3.062
## proj_diff:stereo_diff:inoutgroup_effects -0.45101 0.12942 -3.485
## proj_diff:stereo_diff:ident_c -0.13300 0.02106 -6.317
## proj_diff:inoutgroup_effects:ident_c 0.22235 0.04288 5.185
## stereo_diff:inoutgroup_effects:ident_c 0.04862 0.05201 0.935
## proj_diff:stereo_diff:inoutgroup_effects:ident_c -0.08537 0.04211 -2.027
## Pr(>|t|)
## (Intercept) < 0.0000000000000002 ***
## proj_diff 0.0000000001328016 ***
## stereo_diff < 0.0000000000000002 ***
## inoutgroup_effects < 0.0000000000000002 ***
## ident_c 0.066426 .
## proj_diff:stereo_diff 0.000720 ***
## proj_diff:inoutgroup_effects 0.0000068773171999 ***
## stereo_diff:inoutgroup_effects 0.0000000000000489 ***
## proj_diff:ident_c 0.0000002049694889 ***
## stereo_diff:ident_c 0.0000000000003477 ***
## inoutgroup_effects:ident_c 0.002204 **
## proj_diff:stereo_diff:inoutgroup_effects 0.000493 ***
## proj_diff:stereo_diff:ident_c 0.0000000002729339 ***
## proj_diff:inoutgroup_effects:ident_c 0.0000002177593906 ***
## stereo_diff:inoutgroup_effects:ident_c 0.349889
## proj_diff:stereo_diff:inoutgroup_effects:ident_c 0.042652 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.8 on 19882 degrees of freedom
## Multiple R-squared: 0.1572, Adjusted R-squared: 0.1566
## F-statistic: 247.3 on 15 and 19882 DF, p-value: < 0.00000000000000022
#main effect of projection
ggplot(downstream_analysis, aes(proj_diff, Contentious)) +
geom_smooth(method = "lm") +
theme_minimal() +
labs(title = "Contentiousness predicted by projection of morals",
subtitle = "Projection is the absolute value of the difference score \nbetween average MFQ scores for target and self",
x = "Projection (Higher numbers indicate lower projection)",
y = "Contentousness")
As counter projection increases, predicted contentiousness with the target while have a conversation increases.
content_overall_2way_group <- effect(term = "proj_diff:inoutgroup_effects",
xlevels = list(inoutgroup_effects = c(-.5,.5)),
mod = content_overall)
content_overall_2way_group <- as.data.frame(content_overall_2way_group)
content_overall_2way_group$group <- as.factor(content_overall_2way_group$inoutgroup_effects)
ggplot(content_overall_2way_group, aes(proj_diff, fit, group = group, color = group)) +
geom_smooth(method = "lm", se = FALSE, size = .7, colour = "black",
aes(linetype = group)) +
theme_minimal() +
scale_linetype_manual("Group", breaks = c(-.5, .5),
labels = c("Out-group", "In-group"),
values = c("solid",
"dashed")) +
labs(title = "Contentiousness with target predicted by projection and target group",
subtitle = "Projection is the absolute value of the difference score between average \nMFQ scores for target and self. Higher scores = greater counter projection.",
x = "Projection (Higher = more counter projection)",
y = "Contentiousness")
Predicted contentiousness with a target starts higher for those considering an out-group target instead of an in-group target and increases gradually as counter projection increases. However, predicted contentiousness increases more quickly as counter projection increases for those considering an in-group target.
content_overall_2way_ident <- effect(term = "proj_diff:ident_c",
xlevels = list(ident_c = c(-1.78, 0, 1.78)),
mod = content_overall)
content_overall_2way_ident <- as.data.frame(content_overall_2way_ident)
content_overall_2way_ident$Identification <- as.factor(content_overall_2way_ident$ident_c)
ggplot(content_overall_2way_ident, aes(proj_diff, fit, group = Identification, color = Identification)) +
geom_smooth(method = "lm", se = FALSE, size = .7, colour = "black",
aes(linetype = Identification)) +
theme_minimal() +
scale_linetype_manual("Identification", breaks = c(-1.78, 0, 1.78),
labels = c("Weak (1 SD below)",
"Average",
"High (1 SD above)"),
values = c("solid",
"dashed",
"dotted")) +
labs(title = "Contentiousness predicted by projection and identification",
subtitle = "Projection is the absolute value of the difference score between average \nMFQ scores for target and self. Higher scores = greater counter projection.",
x = "Projection (Higher = more counter projection)",
y = "Contentiousness")
Predicted contentiousness starts higher for those who are highly identified with their in-group and increases the most as counter projection increases. On the other hand, contentiousness starts the lowest for those who are weakly identified with their in-group and increases the least as counter projection increases.
content_overall_3way <- effect(term = "proj_diff:inoutgroup_effects:ident_c",
xlevels = list(ident_c = c(-1.78, 0, 1.78),
inoutgroup_effects = c(-.5,.5)),
mod = content_overall)
content_overall_3way <- as.data.frame(content_overall_3way)
content_overall_3way$Group <- as.factor(content_overall_3way$inoutgroup_effects)
content_overall_3way$Identification <- as.factor(content_overall_3way$ident_c)
ident_label <- c("-1.78" = "Low identification \n (1 SD below)",
"0" = "Average identification",
"1.78" = "High identificatiion \n(1 SD above)")
ggplot(content_overall_3way, aes(proj_diff,
fit,
group = Group,
color = Group)) +
geom_smooth(method = "lm", se = FALSE, size = .7, colour = "black",
aes(linetype = Group)) +
facet_wrap(~Identification, labeller =
labeller(Identification = ident_label)) +
scale_linetype_manual("Group", breaks = c(-.5, .5),
labels = c("Out-group", "In-group"),
values = c("solid",
"dashed")) +
theme_minimal() +
labs(title = "Contentiousness predicted by projection of morals, \nin-group identification and group membership",
subtitle = "Controlling for stereotyping",
x = "Projection difference scores (higher = more counter projection)",
y = "Contentiousness")
For those considering an out-group target, their predicted contentiousness starts higher than those considering an out-group target. As in-group identification increases, counter projection as less of an impact on predicted contentiousness with those considering out-group targets. Specifically, when weakly identified, as counter projection increases, predicted contentiousness also increases. However, when highly identified, predicted contentiousness stays the same as counter projection increases.
For those considering an in-group target, predicted contentiousness increases as counter projection increases, but more so for those who are highly identified with their in-group, followed by average identifiers, with low identifiers only showing a slight increase as counter projection increases. Interestingly, for those who are highly identified, when counter projection is high, people report higher contentiousness with the in-group target than they would with their out-group target.
content_bind <- lm(Contentious ~ proj_bind_diff*stereo_bind_diff*ident_c*inoutgroup_effects, mfq_binding_diff)
summary(content_bind)
##
## Call:
## lm(formula = Contentious ~ proj_bind_diff * stereo_bind_diff *
## ident_c * inoutgroup_effects, data = mfq_binding_diff)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.3664 -1.5604 0.1586 1.4965 4.0397
##
## Coefficients:
## Estimate Std. Error
## (Intercept) 3.571486 0.039764
## proj_bind_diff 0.335843 0.043125
## stereo_bind_diff 0.402068 0.045177
## ident_c 0.082495 0.023749
## inoutgroup_effects -1.512059 0.079528
## proj_bind_diff:stereo_bind_diff -0.177484 0.038988
## proj_bind_diff:ident_c -0.003608 0.020640
## stereo_bind_diff:ident_c 0.036241 0.025709
## proj_bind_diff:inoutgroup_effects 0.175334 0.086250
## stereo_bind_diff:inoutgroup_effects 0.707526 0.090354
## ident_c:inoutgroup_effects -0.016837 0.047499
## proj_bind_diff:stereo_bind_diff:ident_c -0.008879 0.017100
## proj_bind_diff:stereo_bind_diff:inoutgroup_effects -0.331031 0.077976
## proj_bind_diff:ident_c:inoutgroup_effects -0.017748 0.041280
## stereo_bind_diff:ident_c:inoutgroup_effects 0.015075 0.051418
## proj_bind_diff:stereo_bind_diff:ident_c:inoutgroup_effects 0.022667 0.034200
## t value
## (Intercept) 89.817
## proj_bind_diff 7.788
## stereo_bind_diff 8.900
## ident_c 3.474
## inoutgroup_effects -19.013
## proj_bind_diff:stereo_bind_diff -4.552
## proj_bind_diff:ident_c -0.175
## stereo_bind_diff:ident_c 1.410
## proj_bind_diff:inoutgroup_effects 2.033
## stereo_bind_diff:inoutgroup_effects 7.831
## ident_c:inoutgroup_effects -0.354
## proj_bind_diff:stereo_bind_diff:ident_c -0.519
## proj_bind_diff:stereo_bind_diff:inoutgroup_effects -4.245
## proj_bind_diff:ident_c:inoutgroup_effects -0.430
## stereo_bind_diff:ident_c:inoutgroup_effects 0.293
## proj_bind_diff:stereo_bind_diff:ident_c:inoutgroup_effects 0.663
## Pr(>|t|)
## (Intercept) < 0.0000000000000002
## proj_bind_diff 0.00000000000000739
## stereo_bind_diff < 0.0000000000000002
## ident_c 0.000515
## inoutgroup_effects < 0.0000000000000002
## proj_bind_diff:stereo_bind_diff 0.00000535985998455
## proj_bind_diff:ident_c 0.861229
## stereo_bind_diff:ident_c 0.158670
## proj_bind_diff:inoutgroup_effects 0.042091
## stereo_bind_diff:inoutgroup_effects 0.00000000000000527
## ident_c:inoutgroup_effects 0.722985
## proj_bind_diff:stereo_bind_diff:ident_c 0.603621
## proj_bind_diff:stereo_bind_diff:inoutgroup_effects 0.00002199662490525
## proj_bind_diff:ident_c:inoutgroup_effects 0.667235
## stereo_bind_diff:ident_c:inoutgroup_effects 0.769385
## proj_bind_diff:stereo_bind_diff:ident_c:inoutgroup_effects 0.507492
##
## (Intercept) ***
## proj_bind_diff ***
## stereo_bind_diff ***
## ident_c ***
## inoutgroup_effects ***
## proj_bind_diff:stereo_bind_diff ***
## proj_bind_diff:ident_c
## stereo_bind_diff:ident_c
## proj_bind_diff:inoutgroup_effects *
## stereo_bind_diff:inoutgroup_effects ***
## ident_c:inoutgroup_effects
## proj_bind_diff:stereo_bind_diff:ident_c
## proj_bind_diff:stereo_bind_diff:inoutgroup_effects ***
## proj_bind_diff:ident_c:inoutgroup_effects
## stereo_bind_diff:ident_c:inoutgroup_effects
## proj_bind_diff:stereo_bind_diff:ident_c:inoutgroup_effects
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.797 on 11926 degrees of freedom
## Multiple R-squared: 0.1601, Adjusted R-squared: 0.159
## F-statistic: 151.5 on 15 and 11926 DF, p-value: < 0.00000000000000022
ggplot(mfq_binding_diff, aes(proj_bind_diff, Contentious)) +
geom_smooth(method = "lm") +
theme_minimal() +
labs(title = "Contentiousness predicted by projection of binding morals",
subtitle = "Projection is the absolute value of the difference score \nbetween average MFQ scores for target and self",
x = "Projection (Higher numbers indicate lower projection)",
y = "Contentousness")
As counter projection increases, predicted contentiousness with the target while have a conversation increases.
content_bind_2way_group <- effect(term = "proj_bind_diff:inoutgroup_effects",
xlevels = list(inoutgroup_effects = c(-.5,.5)),
mod = content_bind)
content_bind_2way_group <- as.data.frame(content_bind_2way_group)
content_bind_2way_group$group <- as.factor(content_bind_2way_group$inoutgroup_effects)
ggplot(content_bind_2way_group, aes(proj_bind_diff, fit, group = group, color = group)) +
geom_smooth(method = "lm", se = FALSE, size = .7, colour = "black",
aes(linetype = group)) +
theme_minimal() +
scale_linetype_manual("Group", breaks = c(-.5, .5),
labels = c("Out-group", "In-group"),
values = c("solid",
"dashed")) +
labs(title = "Contentiousness with target predicted by projection of binding morals and target group",
subtitle = "Projection is the absolute value of the difference score between average \nMFQ scores for target and self. Higher scores = greater counter projection.",
x = "Projection (Higher = more counter projection)",
y = "Contentiousness")
Predicted contentiousness was higher for out-group targets than in-group targets and, as counter projection of binding morals increases, contentiousness increases more for out-group targets as well.
content_indiv <- lm(Contentious ~ proj_indiv_diff*stereo_indiv_diff*ident_c*inoutgroup_effects, mfq_individual_diff)
summary(content_indiv)
##
## Call:
## lm(formula = Contentious ~ proj_indiv_diff * stereo_indiv_diff *
## ident_c * inoutgroup_effects, data = mfq_individual_diff)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.6935 -1.5171 0.1004 1.5046 4.0585
##
## Coefficients: (4 not defined because of singularities)
## Estimate
## (Intercept) 3.748895
## proj_indiv_diff 0.587006
## stereo_indiv_diff NA
## ident_c 0.059289
## inoutgroup_effects -1.484698
## proj_indiv_diff:stereo_indiv_diff -0.286512
## proj_indiv_diff:ident_c 0.132058
## stereo_indiv_diff:ident_c NA
## proj_indiv_diff:inoutgroup_effects 1.019028
## stereo_indiv_diff:inoutgroup_effects NA
## ident_c:inoutgroup_effects 0.001465
## proj_indiv_diff:stereo_indiv_diff:ident_c -0.107484
## proj_indiv_diff:stereo_indiv_diff:inoutgroup_effects -0.697440
## proj_indiv_diff:ident_c:inoutgroup_effects 0.029069
## stereo_indiv_diff:ident_c:inoutgroup_effects NA
## proj_indiv_diff:stereo_indiv_diff:ident_c:inoutgroup_effects -0.100359
## Std. Error t value
## (Intercept) 0.044931 83.437
## proj_indiv_diff 0.101837 5.764
## stereo_indiv_diff NA NA
## ident_c 0.022265 2.663
## inoutgroup_effects 0.089862 -16.522
## proj_indiv_diff:stereo_indiv_diff 0.054685 -5.239
## proj_indiv_diff:ident_c 0.051226 2.578
## stereo_indiv_diff:ident_c NA NA
## proj_indiv_diff:inoutgroup_effects 0.203674 5.003
## stereo_indiv_diff:inoutgroup_effects NA NA
## ident_c:inoutgroup_effects 0.044529 0.033
## proj_indiv_diff:stereo_indiv_diff:ident_c 0.025678 -4.186
## proj_indiv_diff:stereo_indiv_diff:inoutgroup_effects 0.109369 -6.377
## proj_indiv_diff:ident_c:inoutgroup_effects 0.102452 0.284
## stereo_indiv_diff:ident_c:inoutgroup_effects NA NA
## proj_indiv_diff:stereo_indiv_diff:ident_c:inoutgroup_effects 0.051356 -1.954
## Pr(>|t|)
## (Intercept) < 0.0000000000000002
## proj_indiv_diff 0.000000008513
## stereo_indiv_diff NA
## ident_c 0.00776
## inoutgroup_effects < 0.0000000000000002
## proj_indiv_diff:stereo_indiv_diff 0.000000165276
## proj_indiv_diff:ident_c 0.00996
## stereo_indiv_diff:ident_c NA
## proj_indiv_diff:inoutgroup_effects 0.000000575880
## stereo_indiv_diff:inoutgroup_effects NA
## ident_c:inoutgroup_effects 0.97375
## proj_indiv_diff:stereo_indiv_diff:ident_c 0.000028720025
## proj_indiv_diff:stereo_indiv_diff:inoutgroup_effects 0.000000000191
## proj_indiv_diff:ident_c:inoutgroup_effects 0.77662
## stereo_indiv_diff:ident_c:inoutgroup_effects NA
## proj_indiv_diff:stereo_indiv_diff:ident_c:inoutgroup_effects 0.05072
##
## (Intercept) ***
## proj_indiv_diff ***
## stereo_indiv_diff
## ident_c **
## inoutgroup_effects ***
## proj_indiv_diff:stereo_indiv_diff ***
## proj_indiv_diff:ident_c **
## stereo_indiv_diff:ident_c
## proj_indiv_diff:inoutgroup_effects ***
## stereo_indiv_diff:inoutgroup_effects
## ident_c:inoutgroup_effects
## proj_indiv_diff:stereo_indiv_diff:ident_c ***
## proj_indiv_diff:stereo_indiv_diff:inoutgroup_effects ***
## proj_indiv_diff:ident_c:inoutgroup_effects
## stereo_indiv_diff:ident_c:inoutgroup_effects
## proj_indiv_diff:stereo_indiv_diff:ident_c:inoutgroup_effects .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.799 on 7944 degrees of freedom
## Multiple R-squared: 0.1593, Adjusted R-squared: 0.1582
## F-statistic: 136.9 on 11 and 7944 DF, p-value: < 0.00000000000000022
treat_overall <- lm(Treatment ~ proj_diff*stereo_diff*inoutgroup_effects*ident_c, downstream_analysis)
summary(treat_overall)
##
## Call:
## lm(formula = Treatment ~ proj_diff * stereo_diff * inoutgroup_effects *
## ident_c, data = downstream_analysis)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.6108 -0.8958 0.1467 0.8811 3.9993
##
## Coefficients:
## Estimate Std. Error t value
## (Intercept) 5.03368 0.02348 214.380
## proj_diff -0.03487 0.03884 -0.898
## stereo_diff -0.12835 0.03953 -3.247
## inoutgroup_effects 2.06058 0.04696 43.879
## ident_c 0.06298 0.01263 4.985
## proj_diff:stereo_diff -0.18435 0.05041 -3.657
## proj_diff:inoutgroup_effects 0.49875 0.07769 6.420
## stereo_diff:inoutgroup_effects 0.50920 0.07906 6.441
## proj_diff:ident_c -0.10189 0.01670 -6.100
## stereo_diff:ident_c 0.05234 0.02026 2.583
## inoutgroup_effects:ident_c 0.29728 0.02527 11.766
## proj_diff:stereo_diff:inoutgroup_effects -0.52275 0.10083 -5.185
## proj_diff:stereo_diff:ident_c 0.01939 0.01640 1.182
## proj_diff:inoutgroup_effects:ident_c -0.04374 0.03341 -1.309
## stereo_diff:inoutgroup_effects:ident_c -0.08137 0.04052 -2.008
## proj_diff:stereo_diff:inoutgroup_effects:ident_c 0.05451 0.03281 1.662
## Pr(>|t|)
## (Intercept) < 0.0000000000000002 ***
## proj_diff 0.369299
## stereo_diff 0.001169 **
## inoutgroup_effects < 0.0000000000000002 ***
## ident_c 0.000000624328 ***
## proj_diff:stereo_diff 0.000256 ***
## proj_diff:inoutgroup_effects 0.000000000139 ***
## stereo_diff:inoutgroup_effects 0.000000000122 ***
## proj_diff:ident_c 0.000000001083 ***
## stereo_diff:ident_c 0.009798 **
## inoutgroup_effects:ident_c < 0.0000000000000002 ***
## proj_diff:stereo_diff:inoutgroup_effects 0.000000218685 ***
## proj_diff:stereo_diff:ident_c 0.237322
## proj_diff:inoutgroup_effects:ident_c 0.190411
## stereo_diff:inoutgroup_effects:ident_c 0.044643 *
## proj_diff:stereo_diff:inoutgroup_effects:ident_c 0.096613 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.402 on 19882 degrees of freedom
## Multiple R-squared: 0.4673, Adjusted R-squared: 0.4669
## F-statistic: 1163 on 15 and 19882 DF, p-value: < 0.00000000000000022
No main effect for treatment - the only outcome in which this occurs.
treat_overall_2way_group <- effect(term = "proj_diff:inoutgroup_effects",
xlevels = list(inoutgroup_effects = c(-.5,.5)),
mod = treat_overall)
treat_overall_2way_group <- as.data.frame(treat_overall_2way_group)
treat_overall_2way_group$group <- as.factor(treat_overall_2way_group$inoutgroup_effects)
ggplot(treat_overall_2way_group, aes(proj_diff, fit, group = group, color = group)) +
geom_smooth(method = "lm", se = FALSE, size = .7, colour = "black",
aes(linetype = group)) +
theme_minimal() +
scale_linetype_manual("Group", breaks = c(-.5, .5),
labels = c("Out-group", "In-group"),
values = c("solid",
"dashed")) +
labs(title = "Treatment by target predicted by projection and target group",
subtitle = "Projection is the absolute value of the difference score between average \nMFQ scores for target and self. Higher scores = greater counter projection.",
x = "Projection (Higher = more counter projection)",
y = "Treatment")
Participants expected the target to treat them better in the in-group than the out-group. As counter projection increased, expected treatment decreased for the out-group, while only barely decreasing for the in-group.
treat_overall_2way_ident <- effect(term = "proj_diff:ident_c",
xlevels = list(ident_c = c(-1.78, 0, 1.78)),
mod = treat_overall)
treat_overall_2way_ident <- as.data.frame(treat_overall_2way_ident)
treat_overall_2way_ident$Identification <- as.factor(treat_overall_2way_ident$ident_c)
ggplot(treat_overall_2way_ident, aes(proj_diff, fit, group = Identification, color = Identification)) +
geom_smooth(method = "lm", se = FALSE, size = .7, colour = "black",
aes(linetype = Identification)) +
theme_minimal() +
scale_linetype_manual("Identification", breaks = c(-1.78, 0, 1.78),
labels = c("Weak (1 SD below)",
"Average",
"High (1 SD above)"),
values = c("solid",
"dashed",
"dotted")) +
labs(title = "Treatment by target predicted by projection and identification",
subtitle = "Projection is the absolute value of the difference score between average \nMFQ scores for target and self. Higher scores = greater counter projection.",
x = "Projection (Higher = more counter projection)",
y = "Treatment")
For people who weakly identify with their in-group, as counter projection increases, expected treatment slightly increases. However, for people who identify with their in-group an average amount or highly, as counter projection increases, expected treatment decreases, with it decreasing the most for people who highly identified with their in-group.
treat_bind <- lm(Treatment ~ proj_bind_diff*stereo_bind_diff*inoutgroup_effects*ident_c, mfq_binding_diff)
summary(treat_bind)
##
## Call:
## lm(formula = Treatment ~ proj_bind_diff * stereo_bind_diff *
## inoutgroup_effects * ident_c, data = mfq_binding_diff)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.0579 -0.9026 0.0396 0.8792 4.1841
##
## Coefficients:
## Estimate Std. Error
## (Intercept) 5.166238 0.030315
## proj_bind_diff -0.052612 0.032878
## stereo_bind_diff -0.102178 0.034442
## inoutgroup_effects 2.037037 0.060630
## ident_c 0.034729 0.018106
## proj_bind_diff:stereo_bind_diff -0.028398 0.029724
## proj_bind_diff:inoutgroup_effects 0.098765 0.065755
## stereo_bind_diff:inoutgroup_effects 0.001159 0.068884
## proj_bind_diff:ident_c -0.023310 0.015735
## stereo_bind_diff:ident_c -0.008528 0.019600
## inoutgroup_effects:ident_c 0.176580 0.036212
## proj_bind_diff:stereo_bind_diff:inoutgroup_effects 0.077894 0.059447
## proj_bind_diff:stereo_bind_diff:ident_c 0.032445 0.013037
## proj_bind_diff:inoutgroup_effects:ident_c 0.187069 0.031471
## stereo_bind_diff:inoutgroup_effects:ident_c -0.050736 0.039200
## proj_bind_diff:stereo_bind_diff:inoutgroup_effects:ident_c 0.002489 0.026073
## t value
## (Intercept) 170.419
## proj_bind_diff -1.600
## stereo_bind_diff -2.967
## inoutgroup_effects 33.598
## ident_c 1.918
## proj_bind_diff:stereo_bind_diff -0.955
## proj_bind_diff:inoutgroup_effects 1.502
## stereo_bind_diff:inoutgroup_effects 0.017
## proj_bind_diff:ident_c -1.481
## stereo_bind_diff:ident_c -0.435
## inoutgroup_effects:ident_c 4.876
## proj_bind_diff:stereo_bind_diff:inoutgroup_effects 1.310
## proj_bind_diff:stereo_bind_diff:ident_c 2.489
## proj_bind_diff:inoutgroup_effects:ident_c 5.944
## stereo_bind_diff:inoutgroup_effects:ident_c -1.294
## proj_bind_diff:stereo_bind_diff:inoutgroup_effects:ident_c 0.095
## Pr(>|t|)
## (Intercept) < 0.0000000000000002
## proj_bind_diff 0.10957
## stereo_bind_diff 0.00302
## inoutgroup_effects < 0.0000000000000002
## ident_c 0.05512
## proj_bind_diff:stereo_bind_diff 0.33939
## proj_bind_diff:inoutgroup_effects 0.13312
## stereo_bind_diff:inoutgroup_effects 0.98658
## proj_bind_diff:ident_c 0.13852
## stereo_bind_diff:ident_c 0.66349
## inoutgroup_effects:ident_c 0.00000109495
## proj_bind_diff:stereo_bind_diff:inoutgroup_effects 0.19012
## proj_bind_diff:stereo_bind_diff:ident_c 0.01283
## proj_bind_diff:inoutgroup_effects:ident_c 0.00000000285
## stereo_bind_diff:inoutgroup_effects:ident_c 0.19559
## proj_bind_diff:stereo_bind_diff:inoutgroup_effects:ident_c 0.92396
##
## (Intercept) ***
## proj_bind_diff
## stereo_bind_diff **
## inoutgroup_effects ***
## ident_c .
## proj_bind_diff:stereo_bind_diff
## proj_bind_diff:inoutgroup_effects
## stereo_bind_diff:inoutgroup_effects
## proj_bind_diff:ident_c
## stereo_bind_diff:ident_c
## inoutgroup_effects:ident_c ***
## proj_bind_diff:stereo_bind_diff:inoutgroup_effects
## proj_bind_diff:stereo_bind_diff:ident_c *
## proj_bind_diff:inoutgroup_effects:ident_c ***
## stereo_bind_diff:inoutgroup_effects:ident_c
## proj_bind_diff:stereo_bind_diff:inoutgroup_effects:ident_c
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.37 on 11926 degrees of freedom
## Multiple R-squared: 0.4915, Adjusted R-squared: 0.4909
## F-statistic: 768.6 on 15 and 11926 DF, p-value: < 0.00000000000000022
treat_bind_3way <- effect(term = "proj_bind_diff:inoutgroup_effects:ident_c",
xlevels = list(ident_c = c(-1.78, 0, 1.78),
inoutgroup_effects = c(-.5,.5)),
mod = treat_bind)
treat_bind_3way <- as.data.frame(treat_bind_3way)
treat_bind_3way$Group <- as.factor(treat_bind_3way$inoutgroup_effects)
treat_bind_3way$Identification <- as.factor(treat_bind_3way$ident_c)
ident_label <- c("-1.78" = "Low identification \n (1 SD below)",
"0" = "Average identification",
"1.78" = "High identificatiion \n(1 SD above)")
ggplot(treat_bind_3way, aes(proj_bind_diff,
fit,
group = Group,
color = Group)) +
geom_smooth(method = "lm", se = FALSE, size = .7, colour = "black",
aes(linetype = Group)) +
facet_wrap(~Identification, labeller =
labeller(Identification = ident_label)) +
scale_linetype_manual("Group", breaks = c(-.5, .5),
labels = c("Out-group", "In-group"),
values = c("solid",
"dashed")) +
theme_minimal() +
labs(title = "Treatment by target predicted by projection of binding morals, \nin-group identification and group membership",
subtitle = "Controlling for stereotyping",
x = "Projection difference scores (higher = more counter projection)",
y = "Treatment")
All treatment scores for the in-group condition were higher than those in the out-group. People in the low identification condition expected their in-group to treat them better than their out-group; However, as counter projection increased, expected treatment decreased for those in the in-group condition, while remaining the same for those in the out-group condition. For those who reported average in-group identification, as counter projection increased, people in the out-group condition reported worse treatment scores, while those in the in-group did not report a change. For those who reported high in-group identification, as count projection increased, people expected their out-group to treat them worse, while people expected their in-group to treat them better.
treat_indiv <- lm(Treatment ~ proj_indiv_diff*stereo_indiv_diff*inoutgroup_effects*ident_c, mfq_individual_diff)
summary(treat_indiv)
##
## Call:
## lm(formula = Treatment ~ proj_indiv_diff * stereo_indiv_diff *
## inoutgroup_effects * ident_c, data = mfq_individual_diff)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.3671 -0.8971 0.0353 0.8282 4.7280
##
## Coefficients: (4 not defined because of singularities)
## Estimate
## (Intercept) 5.219881
## proj_indiv_diff -0.205322
## stereo_indiv_diff NA
## inoutgroup_effects 1.739202
## ident_c -0.004403
## proj_indiv_diff:stereo_indiv_diff -0.068976
## proj_indiv_diff:inoutgroup_effects 0.795450
## stereo_indiv_diff:inoutgroup_effects NA
## proj_indiv_diff:ident_c 0.038730
## stereo_indiv_diff:ident_c NA
## inoutgroup_effects:ident_c 0.262681
## proj_indiv_diff:stereo_indiv_diff:inoutgroup_effects -0.143315
## proj_indiv_diff:stereo_indiv_diff:ident_c 0.026045
## proj_indiv_diff:inoutgroup_effects:ident_c 0.020209
## stereo_indiv_diff:inoutgroup_effects:ident_c NA
## proj_indiv_diff:stereo_indiv_diff:inoutgroup_effects:ident_c 0.037263
## Std. Error t value
## (Intercept) 0.033704 154.873
## proj_indiv_diff 0.076391 -2.688
## stereo_indiv_diff NA NA
## inoutgroup_effects 0.067409 25.801
## ident_c 0.016701 -0.264
## proj_indiv_diff:stereo_indiv_diff 0.041021 -1.682
## proj_indiv_diff:inoutgroup_effects 0.152783 5.206
## stereo_indiv_diff:inoutgroup_effects NA NA
## proj_indiv_diff:ident_c 0.038426 1.008
## stereo_indiv_diff:ident_c NA NA
## inoutgroup_effects:ident_c 0.033403 7.864
## proj_indiv_diff:stereo_indiv_diff:inoutgroup_effects 0.082041 -1.747
## proj_indiv_diff:stereo_indiv_diff:ident_c 0.019262 1.352
## proj_indiv_diff:inoutgroup_effects:ident_c 0.076853 0.263
## stereo_indiv_diff:inoutgroup_effects:ident_c NA NA
## proj_indiv_diff:stereo_indiv_diff:inoutgroup_effects:ident_c 0.038524 0.967
## Pr(>|t|)
## (Intercept) < 0.0000000000000002
## proj_indiv_diff 0.00721
## stereo_indiv_diff NA
## inoutgroup_effects < 0.0000000000000002
## ident_c 0.79207
## proj_indiv_diff:stereo_indiv_diff 0.09270
## proj_indiv_diff:inoutgroup_effects 0.00000019736196861
## stereo_indiv_diff:inoutgroup_effects NA
## proj_indiv_diff:ident_c 0.31353
## stereo_indiv_diff:ident_c NA
## inoutgroup_effects:ident_c 0.00000000000000421
## proj_indiv_diff:stereo_indiv_diff:inoutgroup_effects 0.08070
## proj_indiv_diff:stereo_indiv_diff:ident_c 0.17636
## proj_indiv_diff:inoutgroup_effects:ident_c 0.79259
## stereo_indiv_diff:inoutgroup_effects:ident_c NA
## proj_indiv_diff:stereo_indiv_diff:inoutgroup_effects:ident_c 0.33344
##
## (Intercept) ***
## proj_indiv_diff **
## stereo_indiv_diff
## inoutgroup_effects ***
## ident_c
## proj_indiv_diff:stereo_indiv_diff .
## proj_indiv_diff:inoutgroup_effects ***
## stereo_indiv_diff:inoutgroup_effects
## proj_indiv_diff:ident_c
## stereo_indiv_diff:ident_c
## inoutgroup_effects:ident_c ***
## proj_indiv_diff:stereo_indiv_diff:inoutgroup_effects .
## proj_indiv_diff:stereo_indiv_diff:ident_c
## proj_indiv_diff:inoutgroup_effects:ident_c
## stereo_indiv_diff:inoutgroup_effects:ident_c
## proj_indiv_diff:stereo_indiv_diff:inoutgroup_effects:ident_c
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.35 on 7944 degrees of freedom
## Multiple R-squared: 0.5074, Adjusted R-squared: 0.5067
## F-statistic: 743.8 on 11 and 7944 DF, p-value: < 0.00000000000000022
valid_overall <- lm(Valid ~ proj_diff*stereo_diff*inoutgroup_effects*ident_c, downstream_analysis)
summary(valid_overall)
##
## Call:
## lm(formula = Valid ~ proj_diff * stereo_diff * inoutgroup_effects *
## ident_c, data = downstream_analysis)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.5140 -0.8753 0.2023 0.8358 4.0343
##
## Coefficients:
## Estimate Std. Error t value
## (Intercept) 5.065294 0.024030 210.791
## proj_diff -0.109852 0.039753 -2.763
## stereo_diff -0.262408 0.040456 -6.486
## inoutgroup_effects 2.345308 0.048060 48.800
## ident_c 0.157067 0.012929 12.148
## proj_diff:stereo_diff 0.018244 0.051594 0.354
## proj_diff:inoutgroup_effects 0.253932 0.079506 3.194
## stereo_diff:inoutgroup_effects 0.216820 0.080911 2.680
## proj_diff:ident_c -0.063391 0.017095 -3.708
## stereo_diff:ident_c -0.024831 0.020736 -1.197
## inoutgroup_effects:ident_c -0.071014 0.025858 -2.746
## proj_diff:stereo_diff:inoutgroup_effects -0.195846 0.103189 -1.898
## proj_diff:stereo_diff:ident_c 0.005674 0.016788 0.338
## proj_diff:inoutgroup_effects:ident_c 0.136726 0.034190 3.999
## stereo_diff:inoutgroup_effects:ident_c 0.203792 0.041472 4.914
## proj_diff:stereo_diff:inoutgroup_effects:ident_c -0.027411 0.033576 -0.816
## Pr(>|t|)
## (Intercept) < 0.0000000000000002 ***
## proj_diff 0.005727 **
## stereo_diff 0.00000000009 ***
## inoutgroup_effects < 0.0000000000000002 ***
## ident_c < 0.0000000000000002 ***
## proj_diff:stereo_diff 0.723638
## proj_diff:inoutgroup_effects 0.001406 **
## stereo_diff:inoutgroup_effects 0.007374 **
## proj_diff:ident_c 0.000209 ***
## stereo_diff:ident_c 0.231136
## inoutgroup_effects:ident_c 0.006033 **
## proj_diff:stereo_diff:inoutgroup_effects 0.057719 .
## proj_diff:stereo_diff:ident_c 0.735376
## proj_diff:inoutgroup_effects:ident_c 0.00006384085 ***
## stereo_diff:inoutgroup_effects:ident_c 0.00000089938 ***
## proj_diff:stereo_diff:inoutgroup_effects:ident_c 0.414284
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.435 on 19882 degrees of freedom
## Multiple R-squared: 0.4734, Adjusted R-squared: 0.473
## F-statistic: 1191 on 15 and 19882 DF, p-value: < 0.00000000000000022
ggplot(downstream_analysis, aes(proj_diff, Valid)) +
geom_smooth(method = "lm") +
theme_minimal() +
labs(title = "Validity of target's position predicted by projection of morals",
subtitle = "Projection is the absolute value of the difference score \nbetween average MFQ scores for target and self",
x = "Projection (Higher numbers indicate lower projection)",
y = "Validity")
As counter projection increases, validity of the target’s position decreases.
valid_overall_2way_group <- effect(term = "proj_diff:inoutgroup_effects",
xlevels = list(inoutgroup_effects = c(-.5,.5)),
mod = valid_overall)
valid_overall_2way_group <- as.data.frame(valid_overall_2way_group)
valid_overall_2way_group$group <- as.factor(valid_overall_2way_group$inoutgroup_effects)
ggplot(valid_overall_2way_group, aes(proj_diff, fit, group = group, color = group)) +
geom_smooth(method = "lm", se = FALSE, size = .7, colour = "black",
aes(linetype = group)) +
theme_minimal() +
scale_linetype_manual("Group", breaks = c(-.5, .5),
labels = c("Out-group", "In-group"),
values = c("solid",
"dashed")) +
labs(title = "Validity of target's position predicted by projection and target group",
subtitle = "Projection is the absolute value of the difference score between average \nMFQ scores for target and self. Higher scores = greater counter projection.",
x = "Projection (Higher = more counter projection)",
y = "Validity")
People report higher validity of target’s position in the in-group condition than out-group condition and, as counter projection increases, validity decreases more in the out-group condition than the in-group condition.
valid_overall_2way_ident <- effect(term = "proj_diff:ident_c",
xlevels = list(ident_c = c(-1.78, 0, 1.78)),
mod = valid_overall)
valid_overall_2way_ident <- as.data.frame(valid_overall_2way_ident)
valid_overall_2way_ident$Identification <- as.factor(valid_overall_2way_ident$ident_c)
ggplot(valid_overall_2way_ident, aes(proj_diff, fit, group = Identification, color = Identification)) +
geom_smooth(method = "lm", se = FALSE, size = .7, colour = "black",
aes(linetype = Identification)) +
theme_minimal() +
scale_linetype_manual("Identification", breaks = c(-1.78, 0, 1.78),
labels = c("Weak (1 SD below)",
"Average",
"High (1 SD above)"),
values = c("solid",
"dashed",
"dotted")) +
labs(title = "Validity of target's position predicted by projection and identification",
subtitle = "Projection is the absolute value of the difference score between average \nMFQ scores for target and self. Higher scores = greater counter projection.",
x = "Projection (Higher = more counter projection)",
y = "Validity")
As counter projection increases, those who report highly identifying with their in-group report the largest decrease in validity of the target’s opinion, followed by those who identify with their in-group an average amount. Those who weakly identify with their in-group actually show a slight increase in reported validity as counter projection increases.
valid_overall_3way <- effect(term = "proj_diff:inoutgroup_effects:ident_c",
xlevels = list(ident_c = c(-1.78, 0, 1.78),
inoutgroup_effects = c(-.5,.5)),
mod = valid_overall)
valid_overall_3way <- as.data.frame(valid_overall_3way)
valid_overall_3way$Group <- as.factor(valid_overall_3way$inoutgroup_effects)
valid_overall_3way$Identification <- as.factor(valid_overall_3way$ident_c)
ident_label <- c("-1.78" = "Low identification \n (1 SD below)",
"0" = "Average identification",
"1.78" = "High identificatiion \n(1 SD above)")
ggplot(valid_overall_3way, aes(proj_diff,
fit,
group = Group,
color = Group)) +
geom_smooth(method = "lm", se = FALSE, size = .7, colour = "black",
aes(linetype = Group)) +
facet_wrap(~Identification, labeller =
labeller(Identification = ident_label)) +
scale_linetype_manual("Group", breaks = c(-.5, .5),
labels = c("Out-group", "In-group"),
values = c("solid",
"dashed")) +
theme_minimal() +
labs(title = "Validity of target's position predicted by projection of morals, \nin-group identification and group membership",
subtitle = "Controlling for stereotyping",
x = "Projection difference scores (higher = more counter projection)",
y = "Validity")
For those in the in-group position, regardless of in-group identification, as counter projection increases, reported validity decreases only slightly. However, low in-group identifiers overall reported lower validity of their in-group’s opinion that high identifiers. For those in the out-group condition, as counter projection increased, low in-group identifiers report target validity increasing. However, both average and high in-group identifiers report decreases in target validity as counter projection increases, with high in-group identifiers exhibiting the largest decrease.
valid_bind <- lm(Valid~ proj_bind_diff*stereo_bind_diff*inoutgroup_effects*ident_c, mfq_binding_diff)
summary(valid_bind)
##
## Call:
## lm(formula = Valid ~ proj_bind_diff * stereo_bind_diff * inoutgroup_effects *
## ident_c, data = mfq_binding_diff)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.2497 -0.9416 0.0490 0.8386 4.2143
##
## Coefficients:
## Estimate Std. Error
## (Intercept) 5.261115 0.030950
## proj_bind_diff -0.217809 0.033566
## stereo_bind_diff -0.096900 0.035163
## inoutgroup_effects 2.005359 0.061899
## ident_c 0.107428 0.018485
## proj_bind_diff:stereo_bind_diff 0.025207 0.030346
## proj_bind_diff:inoutgroup_effects 0.301218 0.067132
## stereo_bind_diff:inoutgroup_effects -0.048984 0.070326
## proj_bind_diff:ident_c 0.002138 0.016065
## stereo_bind_diff:ident_c -0.034628 0.020010
## inoutgroup_effects:ident_c -0.083529 0.036970
## proj_bind_diff:stereo_bind_diff:inoutgroup_effects 0.078145 0.060692
## proj_bind_diff:stereo_bind_diff:ident_c 0.022681 0.013310
## proj_bind_diff:inoutgroup_effects:ident_c 0.239194 0.032129
## stereo_bind_diff:inoutgroup_effects:ident_c 0.095248 0.040021
## proj_bind_diff:stereo_bind_diff:inoutgroup_effects:ident_c -0.039394 0.026619
## t value
## (Intercept) 169.990
## proj_bind_diff -6.489
## stereo_bind_diff -2.756
## inoutgroup_effects 32.397
## ident_c 5.812
## proj_bind_diff:stereo_bind_diff 0.831
## proj_bind_diff:inoutgroup_effects 4.487
## stereo_bind_diff:inoutgroup_effects -0.697
## proj_bind_diff:ident_c 0.133
## stereo_bind_diff:ident_c -1.730
## inoutgroup_effects:ident_c -2.259
## proj_bind_diff:stereo_bind_diff:inoutgroup_effects 1.288
## proj_bind_diff:stereo_bind_diff:ident_c 1.704
## proj_bind_diff:inoutgroup_effects:ident_c 7.445
## stereo_bind_diff:inoutgroup_effects:ident_c 2.380
## proj_bind_diff:stereo_bind_diff:inoutgroup_effects:ident_c -1.480
## Pr(>|t|)
## (Intercept) < 0.0000000000000002
## proj_bind_diff 0.000000000089830
## stereo_bind_diff 0.00586
## inoutgroup_effects < 0.0000000000000002
## ident_c 0.000000006344128
## proj_bind_diff:stereo_bind_diff 0.40618
## proj_bind_diff:inoutgroup_effects 0.000007291765350
## stereo_bind_diff:inoutgroup_effects 0.48611
## proj_bind_diff:ident_c 0.89415
## stereo_bind_diff:ident_c 0.08357
## inoutgroup_effects:ident_c 0.02388
## proj_bind_diff:stereo_bind_diff:inoutgroup_effects 0.19792
## proj_bind_diff:stereo_bind_diff:ident_c 0.08839
## proj_bind_diff:inoutgroup_effects:ident_c 0.000000000000104
## stereo_bind_diff:inoutgroup_effects:ident_c 0.01733
## proj_bind_diff:stereo_bind_diff:inoutgroup_effects:ident_c 0.13892
##
## (Intercept) ***
## proj_bind_diff ***
## stereo_bind_diff **
## inoutgroup_effects ***
## ident_c ***
## proj_bind_diff:stereo_bind_diff
## proj_bind_diff:inoutgroup_effects ***
## stereo_bind_diff:inoutgroup_effects
## proj_bind_diff:ident_c
## stereo_bind_diff:ident_c .
## inoutgroup_effects:ident_c *
## proj_bind_diff:stereo_bind_diff:inoutgroup_effects
## proj_bind_diff:stereo_bind_diff:ident_c .
## proj_bind_diff:inoutgroup_effects:ident_c ***
## stereo_bind_diff:inoutgroup_effects:ident_c *
## proj_bind_diff:stereo_bind_diff:inoutgroup_effects:ident_c
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.399 on 11926 degrees of freedom
## Multiple R-squared: 0.5, Adjusted R-squared: 0.4994
## F-statistic: 795.1 on 15 and 11926 DF, p-value: < 0.00000000000000022
#main effect of projection
ggplot(mfq_binding_diff, aes(proj_bind_diff, Valid)) +
geom_smooth(method = "lm") +
theme_minimal() +
labs(title = "Validity of target's position predicted by projection of binding morals",
subtitle = "Projection is the absolute value of the difference score \nbetween average MFQ binding scores for target and self",
x = "Projection (Higher numbers indicate lower projection)",
y = "Validity")
As counter projection of binding morals increases, validity decreases.
valid_bind_3way <- effect(term = "proj_bind_diff:inoutgroup_effects:ident_c",
xlevels = list(ident_c = c(-1.78, 0, 1.78),
inoutgroup_effects = c(-.5,.5)),
mod = valid_bind)
valid_bind_3way <- as.data.frame(valid_bind_3way)
valid_bind_3way$Group <- as.factor(valid_bind_3way$inoutgroup_effects)
valid_bind_3way$Identification <- as.factor(valid_bind_3way$ident_c)
ident_label <- c("-1.78" = "Low identification \n (1 SD below)",
"0" = "Average identification",
"1.78" = "High identificatiion \n(1 SD above)")
#three-way interaction
ggplot(valid_bind_3way, aes(proj_bind_diff,
fit,
group = Group,
color = Group)) +
geom_smooth(method = "lm", se = FALSE, size = .7, colour = "black",
aes(linetype = Group)) +
facet_wrap(~Identification, labeller =
labeller(Identification = ident_label)) +
scale_linetype_manual("Group", breaks = c(-.5, .5),
labels = c("Out-group", "In-group"),
values = c("solid",
"dashed")) +
theme_minimal() +
labs(title = "Validity of target's position predicted by projection of binding morals, \nin-group identification and group membership",
subtitle = "Controlling for stereotyping",
x = "Projection difference scores (higher = more counter projection)",
y = "Validity")
For those who weakly identify with their in-group, people report higher validity for their in-group than their out-group, but, as counter projection increases, validity for both groups decreases by a similar amount. For people who report average identification with their in-group, they report higher validity for their in-group than their out-group and, as counter projection increases, validity for the in-group does not change, while validity for the out-group decreases. For those who highly identify with their in-group, people report higher validity for their in-group than their out-group and, as counter projection increases, validity for the in-group increases, while validity to the out-group decreases to the lowest reported validity of all conditions.
valid_indiv <- lm(Valid ~ proj_indiv_diff*stereo_indiv_diff*inoutgroup_effects*ident_c, mfq_individual_diff)
summary(valid_indiv)
##
## Call:
## lm(formula = Valid ~ proj_indiv_diff * stereo_indiv_diff * inoutgroup_effects *
## ident_c, data = mfq_individual_diff)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.1614 -0.9591 0.0237 0.8485 4.9006
##
## Coefficients: (4 not defined because of singularities)
## Estimate
## (Intercept) 5.11705
## proj_indiv_diff 0.05548
## stereo_indiv_diff NA
## inoutgroup_effects 1.92719
## ident_c 0.06032
## proj_indiv_diff:stereo_indiv_diff -0.22667
## proj_indiv_diff:inoutgroup_effects 0.93074
## stereo_indiv_diff:inoutgroup_effects NA
## proj_indiv_diff:ident_c -0.02078
## stereo_indiv_diff:ident_c NA
## inoutgroup_effects:ident_c 0.18797
## proj_indiv_diff:stereo_indiv_diff:inoutgroup_effects -0.38056
## proj_indiv_diff:stereo_indiv_diff:ident_c 0.06001
## proj_indiv_diff:inoutgroup_effects:ident_c -0.22630
## stereo_indiv_diff:inoutgroup_effects:ident_c NA
## proj_indiv_diff:stereo_indiv_diff:inoutgroup_effects:ident_c 0.17629
## Std. Error t value
## (Intercept) 0.03469 147.488
## proj_indiv_diff 0.07864 0.706
## stereo_indiv_diff NA NA
## inoutgroup_effects 0.06939 27.773
## ident_c 0.01719 3.509
## proj_indiv_diff:stereo_indiv_diff 0.04223 -5.368
## proj_indiv_diff:inoutgroup_effects 0.15727 5.918
## stereo_indiv_diff:inoutgroup_effects NA NA
## proj_indiv_diff:ident_c 0.03956 -0.525
## stereo_indiv_diff:ident_c NA NA
## inoutgroup_effects:ident_c 0.03438 5.467
## proj_indiv_diff:stereo_indiv_diff:inoutgroup_effects 0.08445 -4.506
## proj_indiv_diff:stereo_indiv_diff:ident_c 0.01983 3.027
## proj_indiv_diff:inoutgroup_effects:ident_c 0.07911 -2.861
## stereo_indiv_diff:inoutgroup_effects:ident_c NA NA
## proj_indiv_diff:stereo_indiv_diff:inoutgroup_effects:ident_c 0.03966 4.445
## Pr(>|t|)
## (Intercept) < 0.0000000000000002
## proj_indiv_diff 0.480472
## stereo_indiv_diff NA
## inoutgroup_effects < 0.0000000000000002
## ident_c 0.000453
## proj_indiv_diff:stereo_indiv_diff 0.00000008185
## proj_indiv_diff:inoutgroup_effects 0.00000000339
## stereo_indiv_diff:inoutgroup_effects NA
## proj_indiv_diff:ident_c 0.599310
## stereo_indiv_diff:ident_c NA
## inoutgroup_effects:ident_c 0.00000004727
## proj_indiv_diff:stereo_indiv_diff:inoutgroup_effects 0.00000669278
## proj_indiv_diff:stereo_indiv_diff:ident_c 0.002481
## proj_indiv_diff:inoutgroup_effects:ident_c 0.004240
## stereo_indiv_diff:inoutgroup_effects:ident_c NA
## proj_indiv_diff:stereo_indiv_diff:inoutgroup_effects:ident_c 0.00000889167
##
## (Intercept) ***
## proj_indiv_diff
## stereo_indiv_diff
## inoutgroup_effects ***
## ident_c ***
## proj_indiv_diff:stereo_indiv_diff ***
## proj_indiv_diff:inoutgroup_effects ***
## stereo_indiv_diff:inoutgroup_effects
## proj_indiv_diff:ident_c
## stereo_indiv_diff:ident_c
## inoutgroup_effects:ident_c ***
## proj_indiv_diff:stereo_indiv_diff:inoutgroup_effects ***
## proj_indiv_diff:stereo_indiv_diff:ident_c **
## proj_indiv_diff:inoutgroup_effects:ident_c **
## stereo_indiv_diff:inoutgroup_effects:ident_c
## proj_indiv_diff:stereo_indiv_diff:inoutgroup_effects:ident_c ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.389 on 7944 degrees of freedom
## Multiple R-squared: 0.507, Adjusted R-squared: 0.5063
## F-statistic: 742.8 on 11 and 7944 DF, p-value: < 0.00000000000000022
liking_coeff <- lm(Liking ~ MFQSelf_c*`MFQSelf_c:inoutgroup_effects`*`MFQSelf_c:ident_c`*`MFQSelf_c:inoutgroup_effects:ident_c`, data = downstream_coeffs)
summary(liking_coeff)
##
## Call:
## lm(formula = Liking ~ MFQSelf_c * `MFQSelf_c:inoutgroup_effects` *
## `MFQSelf_c:ident_c` * `MFQSelf_c:inoutgroup_effects:ident_c`,
## data = downstream_coeffs)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.8717 -1.0608 -0.0654 1.2492 3.3006
##
## Coefficients: (14 not defined because of singularities)
## Estimate
## (Intercept) 4.21564
## MFQSelf_c 2.11040
## `MFQSelf_c:inoutgroup_effects` NA
## `MFQSelf_c:ident_c` NA
## `MFQSelf_c:inoutgroup_effects:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects` NA
## MFQSelf_c:`MFQSelf_c:ident_c` NA
## `MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## `MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## `MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## MFQSelf_c:`MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## `MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## Std. Error
## (Intercept) 0.07003
## MFQSelf_c 0.24147
## `MFQSelf_c:inoutgroup_effects` NA
## `MFQSelf_c:ident_c` NA
## `MFQSelf_c:inoutgroup_effects:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects` NA
## MFQSelf_c:`MFQSelf_c:ident_c` NA
## `MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## `MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## `MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## MFQSelf_c:`MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## `MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## t value
## (Intercept) 60.20
## MFQSelf_c 8.74
## `MFQSelf_c:inoutgroup_effects` NA
## `MFQSelf_c:ident_c` NA
## `MFQSelf_c:inoutgroup_effects:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects` NA
## MFQSelf_c:`MFQSelf_c:ident_c` NA
## `MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## `MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## `MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## MFQSelf_c:`MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## `MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## Pr(>|t|)
## (Intercept) <0.0000000000000002
## MFQSelf_c <0.0000000000000002
## `MFQSelf_c:inoutgroup_effects` NA
## `MFQSelf_c:ident_c` NA
## `MFQSelf_c:inoutgroup_effects:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects` NA
## MFQSelf_c:`MFQSelf_c:ident_c` NA
## `MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## `MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## `MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## MFQSelf_c:`MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## `MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
##
## (Intercept) ***
## MFQSelf_c ***
## `MFQSelf_c:inoutgroup_effects`
## `MFQSelf_c:ident_c`
## `MFQSelf_c:inoutgroup_effects:ident_c`
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects`
## MFQSelf_c:`MFQSelf_c:ident_c`
## `MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c`
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects:ident_c`
## `MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:inoutgroup_effects:ident_c`
## `MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c`
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c`
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:inoutgroup_effects:ident_c`
## MFQSelf_c:`MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c`
## `MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c`
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c`
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.576 on 667 degrees of freedom
## (5 observations deleted due to missingness)
## Multiple R-squared: 0.1028, Adjusted R-squared: 0.1014
## F-statistic: 76.39 on 1 and 667 DF, p-value: < 0.00000000000000022
ggplot(downstream_coeffs,aes(MFQSelf_c, Liking)) +
geom_smooth(method = "lm") +
labs(title = "Liking predicted by projection coefficient",
subtitle = "Coefficient from initial study model controlling for stereotyping",
x = "Projection coefficient") +
theme_minimal()
diffs <- downstream_analysis %>%
select(subid, proj_diff, stereo_diff)
coeffs_and_diffs <- left_join(downstream_coeffs, diffs) %>% unique()
liking_codiff <- lm(Liking ~ MFQSelf_c*proj_diff, coeffs_and_diffs)
summary(liking_codiff)
##
## Call:
## lm(formula = Liking ~ MFQSelf_c * proj_diff, data = coeffs_and_diffs)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.0886 -1.0369 0.0241 1.1853 3.9471
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.6136 0.1144 40.334 < 0.0000000000000002 ***
## MFQSelf_c 1.8815 0.3948 4.765 0.00000232 ***
## proj_diff -0.6135 0.1292 -4.748 0.00000252 ***
## MFQSelf_c:proj_diff -0.1288 0.5037 -0.256 0.798
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.546 on 665 degrees of freedom
## (5 observations deleted due to missingness)
## Multiple R-squared: 0.1397, Adjusted R-squared: 0.1358
## F-statistic: 35.99 on 3 and 665 DF, p-value: < 0.00000000000000022
Two main effects but no interaction for liking. By the negative coefficient for the similarity score, we can tell that as dissimilarity increases, liking decreases (like the plot we did for the downstream effects using difference scores before).
#getting the coefficients from initial study with stereotyping
coeffs_bind <- coef(is_mfqbinding)$subid
subid <- rownames(coeffs_bind)
coeffs_bind <- cbind(id = subid, coeffs_bind)
coeffs_bind <- coeffs_bind %>% mutate(subid = as.numeric(id))
downstream_coeffs_bind <- left_join(coeffs_bind, tomerge) %>% select(-id) %>% unique()
liking_coeff_bind <- lm(Liking ~ MFQSelf_c*`MFQSelf_c:inoutgroup_effects`*`MFQSelf_c:ident_c`*`MFQSelf_c:inoutgroup_effects:ident_c`, data = downstream_coeffs_bind)
summary(liking_coeff_bind)
##
## Call:
## lm(formula = Liking ~ MFQSelf_c * `MFQSelf_c:inoutgroup_effects` *
## `MFQSelf_c:ident_c` * `MFQSelf_c:inoutgroup_effects:ident_c`,
## data = downstream_coeffs_bind)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.0808 -0.9415 -0.1127 1.3590 3.2279
##
## Coefficients: (14 not defined because of singularities)
## Estimate
## (Intercept) 4.23230
## MFQSelf_c 2.26496
## `MFQSelf_c:inoutgroup_effects` NA
## `MFQSelf_c:ident_c` NA
## `MFQSelf_c:inoutgroup_effects:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects` NA
## MFQSelf_c:`MFQSelf_c:ident_c` NA
## `MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## `MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## `MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## MFQSelf_c:`MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## `MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## Std. Error
## (Intercept) 0.08132
## MFQSelf_c 0.40960
## `MFQSelf_c:inoutgroup_effects` NA
## `MFQSelf_c:ident_c` NA
## `MFQSelf_c:inoutgroup_effects:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects` NA
## MFQSelf_c:`MFQSelf_c:ident_c` NA
## `MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## `MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## `MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## MFQSelf_c:`MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## `MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## t value
## (Intercept) 52.05
## MFQSelf_c 5.53
## `MFQSelf_c:inoutgroup_effects` NA
## `MFQSelf_c:ident_c` NA
## `MFQSelf_c:inoutgroup_effects:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects` NA
## MFQSelf_c:`MFQSelf_c:ident_c` NA
## `MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## `MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## `MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## MFQSelf_c:`MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## `MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## Pr(>|t|)
## (Intercept) < 0.0000000000000002
## MFQSelf_c 0.0000000461
## `MFQSelf_c:inoutgroup_effects` NA
## `MFQSelf_c:ident_c` NA
## `MFQSelf_c:inoutgroup_effects:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects` NA
## MFQSelf_c:`MFQSelf_c:ident_c` NA
## `MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## `MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## `MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## MFQSelf_c:`MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## `MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
##
## (Intercept) ***
## MFQSelf_c ***
## `MFQSelf_c:inoutgroup_effects`
## `MFQSelf_c:ident_c`
## `MFQSelf_c:inoutgroup_effects:ident_c`
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects`
## MFQSelf_c:`MFQSelf_c:ident_c`
## `MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c`
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects:ident_c`
## `MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:inoutgroup_effects:ident_c`
## `MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c`
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c`
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:inoutgroup_effects:ident_c`
## MFQSelf_c:`MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c`
## `MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c`
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c`
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.627 on 667 degrees of freedom
## Multiple R-squared: 0.04383, Adjusted R-squared: 0.0424
## F-statistic: 30.58 on 1 and 667 DF, p-value: 0.00000004608
ggplot(downstream_coeffs_bind,aes(MFQSelf_c, Liking)) +
geom_smooth(method = "lm") +
labs(title = "Liking predicted by projection coefficient",
subtitle = "Coefficient from initial study with binding data only controlling for stereotyping",
x = "Projection coefficient") +
theme_minimal()
This looks pretty much the same as the overall model
liking_coeff_indiv <- lm(Liking ~ MFQSelf_c*`MFQSelf_c:inoutgroup_effects`*`MFQSelf_c:ident_c`*`MFQSelf_c:inoutgroup_effects:ident_c`, data = downstream_coeffs_indiv)
summary(liking_coeff_indiv)
##
## Call:
## lm(formula = Liking ~ MFQSelf_c * `MFQSelf_c:inoutgroup_effects` *
## `MFQSelf_c:ident_c` * `MFQSelf_c:inoutgroup_effects:ident_c`,
## data = downstream_coeffs_indiv)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.0738 -0.8740 -0.1002 1.3210 3.1777
##
## Coefficients: (14 not defined because of singularities)
## Estimate
## (Intercept) 4.10544
## MFQSelf_c 1.94964
## `MFQSelf_c:inoutgroup_effects` NA
## `MFQSelf_c:ident_c` NA
## `MFQSelf_c:inoutgroup_effects:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects` NA
## MFQSelf_c:`MFQSelf_c:ident_c` NA
## `MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## `MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## `MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## MFQSelf_c:`MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## `MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## Std. Error
## (Intercept) 0.09869
## MFQSelf_c 0.35985
## `MFQSelf_c:inoutgroup_effects` NA
## `MFQSelf_c:ident_c` NA
## `MFQSelf_c:inoutgroup_effects:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects` NA
## MFQSelf_c:`MFQSelf_c:ident_c` NA
## `MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## `MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## `MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## MFQSelf_c:`MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## `MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## t value
## (Intercept) 41.598
## MFQSelf_c 5.418
## `MFQSelf_c:inoutgroup_effects` NA
## `MFQSelf_c:ident_c` NA
## `MFQSelf_c:inoutgroup_effects:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects` NA
## MFQSelf_c:`MFQSelf_c:ident_c` NA
## `MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## `MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## `MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## MFQSelf_c:`MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## `MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## Pr(>|t|)
## (Intercept) < 0.0000000000000002
## MFQSelf_c 0.0000000843
## `MFQSelf_c:inoutgroup_effects` NA
## `MFQSelf_c:ident_c` NA
## `MFQSelf_c:inoutgroup_effects:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects` NA
## MFQSelf_c:`MFQSelf_c:ident_c` NA
## `MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## `MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## `MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## MFQSelf_c:`MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## `MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
##
## (Intercept) ***
## MFQSelf_c ***
## `MFQSelf_c:inoutgroup_effects`
## `MFQSelf_c:ident_c`
## `MFQSelf_c:inoutgroup_effects:ident_c`
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects`
## MFQSelf_c:`MFQSelf_c:ident_c`
## `MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c`
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects:ident_c`
## `MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:inoutgroup_effects:ident_c`
## `MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c`
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c`
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:inoutgroup_effects:ident_c`
## MFQSelf_c:`MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c`
## `MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c`
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c`
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.629 on 667 degrees of freedom
## Multiple R-squared: 0.04215, Adjusted R-squared: 0.04072
## F-statistic: 29.35 on 1 and 667 DF, p-value: 0.0000000843
ggplot(downstream_coeffs_indiv,aes(MFQSelf_c, Liking)) +
geom_smooth(method = "lm") +
labs(title = "Liking predicted by projection coefficient",
subtitle = "Coefficient from initial study with individuating data only controlling for stereotyping",
x = "Projection coefficient") +
theme_minimal()
Also pretty much the same as the overall model
discuss_coeffs <- lm(WilltoDiscuss ~ MFQSelf_c*`MFQSelf_c:inoutgroup_effects`*`MFQSelf_c:ident_c`*`MFQSelf_c:inoutgroup_effects:ident_c`, data = downstream_coeffs)
summary(discuss_coeffs)
##
## Call:
## lm(formula = WilltoDiscuss ~ MFQSelf_c * `MFQSelf_c:inoutgroup_effects` *
## `MFQSelf_c:ident_c` * `MFQSelf_c:inoutgroup_effects:ident_c`,
## data = downstream_coeffs)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.5085 -1.1812 0.2831 1.7153 2.6674
##
## Coefficients: (14 not defined because of singularities)
## Estimate
## (Intercept) 4.75088
## MFQSelf_c 0.93792
## `MFQSelf_c:inoutgroup_effects` NA
## `MFQSelf_c:ident_c` NA
## `MFQSelf_c:inoutgroup_effects:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects` NA
## MFQSelf_c:`MFQSelf_c:ident_c` NA
## `MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## `MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## `MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## MFQSelf_c:`MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## `MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## Std. Error
## (Intercept) 0.08478
## MFQSelf_c 0.29231
## `MFQSelf_c:inoutgroup_effects` NA
## `MFQSelf_c:ident_c` NA
## `MFQSelf_c:inoutgroup_effects:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects` NA
## MFQSelf_c:`MFQSelf_c:ident_c` NA
## `MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## `MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## `MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## MFQSelf_c:`MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## `MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## t value
## (Intercept) 56.039
## MFQSelf_c 3.209
## `MFQSelf_c:inoutgroup_effects` NA
## `MFQSelf_c:ident_c` NA
## `MFQSelf_c:inoutgroup_effects:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects` NA
## MFQSelf_c:`MFQSelf_c:ident_c` NA
## `MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## `MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## `MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## MFQSelf_c:`MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## `MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## Pr(>|t|)
## (Intercept) <0.0000000000000002
## MFQSelf_c 0.0014
## `MFQSelf_c:inoutgroup_effects` NA
## `MFQSelf_c:ident_c` NA
## `MFQSelf_c:inoutgroup_effects:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects` NA
## MFQSelf_c:`MFQSelf_c:ident_c` NA
## `MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## `MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## `MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## MFQSelf_c:`MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## `MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
##
## (Intercept) ***
## MFQSelf_c **
## `MFQSelf_c:inoutgroup_effects`
## `MFQSelf_c:ident_c`
## `MFQSelf_c:inoutgroup_effects:ident_c`
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects`
## MFQSelf_c:`MFQSelf_c:ident_c`
## `MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c`
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects:ident_c`
## `MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:inoutgroup_effects:ident_c`
## `MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c`
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c`
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:inoutgroup_effects:ident_c`
## MFQSelf_c:`MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c`
## `MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c`
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c`
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.908 on 667 degrees of freedom
## (5 observations deleted due to missingness)
## Multiple R-squared: 0.0152, Adjusted R-squared: 0.01372
## F-statistic: 10.3 on 1 and 667 DF, p-value: 0.001398
ggplot(downstream_coeffs,aes(MFQSelf_c, WilltoDiscuss)) +
geom_smooth(method = "lm") +
labs(title = "Willigness to have a conversation predicted by projection coefficient",
subtitle = "Coefficient from initial study controlling for stereotyping",
x = "Projection coefficient",
y = "Willingness") +
theme_minimal()
discuss_codiff <- lm(WilltoDiscuss ~ MFQSelf_c*proj_diff, coeffs_and_diffs)
summary(discuss_codiff)
##
## Call:
## lm(formula = WilltoDiscuss ~ MFQSelf_c * proj_diff, data = coeffs_and_diffs)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.7063 -1.2055 0.3149 1.5950 3.4968
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.0611 0.1395 36.281 < 0.0000000000000002 ***
## MFQSelf_c 1.1180 0.4815 2.322 0.02055 *
## proj_diff -0.5069 0.1576 -3.217 0.00136 **
## MFQSelf_c:proj_diff -0.7608 0.6143 -1.238 0.21601
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.885 on 665 degrees of freedom
## (5 observations deleted due to missingness)
## Multiple R-squared: 0.04166, Adjusted R-squared: 0.03734
## F-statistic: 9.637 on 3 and 665 DF, p-value: 0.00000311
Still significant after including the similarity scores, which, again, are also significant but the interaction between the two is not.
discuss_coeff_bind <- lm(WilltoDiscuss ~ MFQSelf_c*`MFQSelf_c:inoutgroup_effects`*`MFQSelf_c:ident_c`*`MFQSelf_c:inoutgroup_effects:ident_c`, data = downstream_coeffs_bind)
summary(discuss_coeff_bind)
##
## Call:
## lm(formula = WilltoDiscuss ~ MFQSelf_c * `MFQSelf_c:inoutgroup_effects` *
## `MFQSelf_c:ident_c` * `MFQSelf_c:inoutgroup_effects:ident_c`,
## data = downstream_coeffs_bind)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.3181 -1.0979 0.2413 1.7868 2.5792
##
## Coefficients: (14 not defined because of singularities)
## Estimate
## (Intercept) 4.75351
## MFQSelf_c 1.04463
## `MFQSelf_c:inoutgroup_effects` NA
## `MFQSelf_c:ident_c` NA
## `MFQSelf_c:inoutgroup_effects:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects` NA
## MFQSelf_c:`MFQSelf_c:ident_c` NA
## `MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## `MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## `MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## MFQSelf_c:`MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## `MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## Std. Error
## (Intercept) 0.09575
## MFQSelf_c 0.48233
## `MFQSelf_c:inoutgroup_effects` NA
## `MFQSelf_c:ident_c` NA
## `MFQSelf_c:inoutgroup_effects:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects` NA
## MFQSelf_c:`MFQSelf_c:ident_c` NA
## `MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## `MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## `MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## MFQSelf_c:`MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## `MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## t value
## (Intercept) 49.643
## MFQSelf_c 2.166
## `MFQSelf_c:inoutgroup_effects` NA
## `MFQSelf_c:ident_c` NA
## `MFQSelf_c:inoutgroup_effects:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects` NA
## MFQSelf_c:`MFQSelf_c:ident_c` NA
## `MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## `MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## `MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## MFQSelf_c:`MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## `MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## Pr(>|t|)
## (Intercept) <0.0000000000000002
## MFQSelf_c 0.0307
## `MFQSelf_c:inoutgroup_effects` NA
## `MFQSelf_c:ident_c` NA
## `MFQSelf_c:inoutgroup_effects:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects` NA
## MFQSelf_c:`MFQSelf_c:ident_c` NA
## `MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## `MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## `MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## MFQSelf_c:`MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## `MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
##
## (Intercept) ***
## MFQSelf_c *
## `MFQSelf_c:inoutgroup_effects`
## `MFQSelf_c:ident_c`
## `MFQSelf_c:inoutgroup_effects:ident_c`
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects`
## MFQSelf_c:`MFQSelf_c:ident_c`
## `MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c`
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects:ident_c`
## `MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:inoutgroup_effects:ident_c`
## `MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c`
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c`
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:inoutgroup_effects:ident_c`
## MFQSelf_c:`MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c`
## `MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c`
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c`
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.916 on 667 degrees of freedom
## Multiple R-squared: 0.006984, Adjusted R-squared: 0.005495
## F-statistic: 4.691 on 1 and 667 DF, p-value: 0.03068
ggplot(downstream_coeffs_bind,aes(MFQSelf_c, WilltoDiscuss)) +
geom_smooth(method = "lm") +
labs(title = "Willingness to have discussion predicted by projection coefficient",
subtitle = "Coefficient from initial study with binding data only controlling for stereotyping",
x = "Projection coefficient",
y = "Willingness") +
theme_minimal()
discuss_coeff_indiv <- lm(WilltoDiscuss ~ MFQSelf_c*`MFQSelf_c:inoutgroup_effects`*`MFQSelf_c:ident_c`*`MFQSelf_c:inoutgroup_effects:ident_c`, data = downstream_coeffs_indiv)
summary(discuss_coeff_indiv)
##
## Call:
## lm(formula = WilltoDiscuss ~ MFQSelf_c * `MFQSelf_c:inoutgroup_effects` *
## `MFQSelf_c:ident_c` * `MFQSelf_c:inoutgroup_effects:ident_c`,
## data = downstream_coeffs_indiv)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.3569 -1.0171 0.1831 1.8784 2.5008
##
## Coefficients: (14 not defined because of singularities)
## Estimate
## (Intercept) 4.7324
## MFQSelf_c 0.7222
## `MFQSelf_c:inoutgroup_effects` NA
## `MFQSelf_c:ident_c` NA
## `MFQSelf_c:inoutgroup_effects:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects` NA
## MFQSelf_c:`MFQSelf_c:ident_c` NA
## `MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## `MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## `MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## MFQSelf_c:`MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## `MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## Std. Error
## (Intercept) 0.1163
## MFQSelf_c 0.4239
## `MFQSelf_c:inoutgroup_effects` NA
## `MFQSelf_c:ident_c` NA
## `MFQSelf_c:inoutgroup_effects:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects` NA
## MFQSelf_c:`MFQSelf_c:ident_c` NA
## `MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## `MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## `MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## MFQSelf_c:`MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## `MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## t value
## (Intercept) 40.702
## MFQSelf_c 1.703
## `MFQSelf_c:inoutgroup_effects` NA
## `MFQSelf_c:ident_c` NA
## `MFQSelf_c:inoutgroup_effects:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects` NA
## MFQSelf_c:`MFQSelf_c:ident_c` NA
## `MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## `MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## `MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## MFQSelf_c:`MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## `MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## Pr(>|t|)
## (Intercept) <0.0000000000000002
## MFQSelf_c 0.0889
## `MFQSelf_c:inoutgroup_effects` NA
## `MFQSelf_c:ident_c` NA
## `MFQSelf_c:inoutgroup_effects:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects` NA
## MFQSelf_c:`MFQSelf_c:ident_c` NA
## `MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## `MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## `MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## MFQSelf_c:`MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## `MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
##
## (Intercept) ***
## MFQSelf_c .
## `MFQSelf_c:inoutgroup_effects`
## `MFQSelf_c:ident_c`
## `MFQSelf_c:inoutgroup_effects:ident_c`
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects`
## MFQSelf_c:`MFQSelf_c:ident_c`
## `MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c`
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects:ident_c`
## `MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:inoutgroup_effects:ident_c`
## `MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c`
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c`
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:inoutgroup_effects:ident_c`
## MFQSelf_c:`MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c`
## `MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c`
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c`
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.919 on 667 degrees of freedom
## Multiple R-squared: 0.004332, Adjusted R-squared: 0.002839
## F-statistic: 2.902 on 1 and 667 DF, p-value: 0.08895
ggplot(downstream_coeffs_indiv,aes(MFQSelf_c, WilltoDiscuss)) +
geom_smooth(method = "lm") +
labs(title = "Willingness to have discussion predicted by projection coefficient",
subtitle = "Coefficient from initial study with individuating data only controlling for stereotyping",
x = "Projection coefficient",
y = "Willingness") +
theme_minimal()
The effect was there with the binding data, but is only marginal with the individuating data.
content_coeffs <- lm(Contentious ~ MFQSelf_c*`MFQSelf_c:inoutgroup_effects`*`MFQSelf_c:ident_c`*`MFQSelf_c:inoutgroup_effects:ident_c`, data = downstream_coeffs)
summary(content_coeffs)
##
## Call:
## lm(formula = Contentious ~ MFQSelf_c * `MFQSelf_c:inoutgroup_effects` *
## `MFQSelf_c:ident_c` * `MFQSelf_c:inoutgroup_effects:ident_c`,
## data = downstream_coeffs)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.3225 -1.6000 0.0448 1.6434 4.1309
##
## Coefficients: (14 not defined because of singularities)
## Estimate
## (Intercept) 4.30714
## MFQSelf_c -1.95072
## `MFQSelf_c:inoutgroup_effects` NA
## `MFQSelf_c:ident_c` NA
## `MFQSelf_c:inoutgroup_effects:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects` NA
## MFQSelf_c:`MFQSelf_c:ident_c` NA
## `MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## `MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## `MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## MFQSelf_c:`MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## `MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## Std. Error
## (Intercept) 0.08439
## MFQSelf_c 0.29096
## `MFQSelf_c:inoutgroup_effects` NA
## `MFQSelf_c:ident_c` NA
## `MFQSelf_c:inoutgroup_effects:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects` NA
## MFQSelf_c:`MFQSelf_c:ident_c` NA
## `MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## `MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## `MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## MFQSelf_c:`MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## `MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## t value
## (Intercept) 51.041
## MFQSelf_c -6.704
## `MFQSelf_c:inoutgroup_effects` NA
## `MFQSelf_c:ident_c` NA
## `MFQSelf_c:inoutgroup_effects:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects` NA
## MFQSelf_c:`MFQSelf_c:ident_c` NA
## `MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## `MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## `MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## MFQSelf_c:`MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## `MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## Pr(>|t|)
## (Intercept) < 0.0000000000000002
## MFQSelf_c 0.0000000000431
## `MFQSelf_c:inoutgroup_effects` NA
## `MFQSelf_c:ident_c` NA
## `MFQSelf_c:inoutgroup_effects:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects` NA
## MFQSelf_c:`MFQSelf_c:ident_c` NA
## `MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## `MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## `MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## MFQSelf_c:`MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## `MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
##
## (Intercept) ***
## MFQSelf_c ***
## `MFQSelf_c:inoutgroup_effects`
## `MFQSelf_c:ident_c`
## `MFQSelf_c:inoutgroup_effects:ident_c`
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects`
## MFQSelf_c:`MFQSelf_c:ident_c`
## `MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c`
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects:ident_c`
## `MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:inoutgroup_effects:ident_c`
## `MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c`
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c`
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:inoutgroup_effects:ident_c`
## MFQSelf_c:`MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c`
## `MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c`
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c`
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.899 on 667 degrees of freedom
## (5 observations deleted due to missingness)
## Multiple R-squared: 0.06313, Adjusted R-squared: 0.06173
## F-statistic: 44.95 on 1 and 667 DF, p-value: 0.00000000004313
ggplot(downstream_coeffs,aes(MFQSelf_c, Contentious)) +
geom_smooth(method = "lm") +
labs(title = "Contentiousness predicted by projection coefficient",
subtitle = "Coefficient from initial study controlling for stereotyping",
x = "Projection coefficient",
y = "Contentiousness") +
theme_minimal()
content_codiff <- lm(Contentious ~ MFQSelf_c*proj_diff, coeffs_and_diffs)
summary(content_codiff)
##
## Call:
## lm(formula = Contentious ~ MFQSelf_c * proj_diff, data = coeffs_and_diffs)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.4950 -1.6050 0.1114 1.5978 3.8107
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4.10228 0.14030 29.240 < 0.0000000000000002 ***
## MFQSelf_c -1.75318 0.48427 -3.620 0.000317 ***
## proj_diff 0.30945 0.15850 1.952 0.051307 .
## MFQSelf_c:proj_diff -0.08051 0.61784 -0.130 0.896364
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.896 on 665 degrees of freedom
## (5 observations deleted due to missingness)
## Multiple R-squared: 0.06925, Adjusted R-squared: 0.06506
## F-statistic: 16.49 on 3 and 665 DF, p-value: 0.0000000002392
Projection is significant, but the difference score is only marginal.
content_coeff_bind <- lm(Contentious ~ MFQSelf_c*`MFQSelf_c:inoutgroup_effects`*`MFQSelf_c:ident_c`*`MFQSelf_c:inoutgroup_effects:ident_c`, data = downstream_coeffs_bind)
summary(content_coeff_bind)
##
## Call:
## lm(formula = Contentious ~ MFQSelf_c * `MFQSelf_c:inoutgroup_effects` *
## `MFQSelf_c:ident_c` * `MFQSelf_c:inoutgroup_effects:ident_c`,
## data = downstream_coeffs_bind)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.8278 -1.6903 0.0235 1.7339 3.5124
##
## Coefficients: (14 not defined because of singularities)
## Estimate
## (Intercept) 4.30173
## MFQSelf_c -2.17300
## `MFQSelf_c:inoutgroup_effects` NA
## `MFQSelf_c:ident_c` NA
## `MFQSelf_c:inoutgroup_effects:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects` NA
## MFQSelf_c:`MFQSelf_c:ident_c` NA
## `MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## `MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## `MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## MFQSelf_c:`MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## `MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## Std. Error
## (Intercept) 0.09663
## MFQSelf_c 0.48674
## `MFQSelf_c:inoutgroup_effects` NA
## `MFQSelf_c:ident_c` NA
## `MFQSelf_c:inoutgroup_effects:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects` NA
## MFQSelf_c:`MFQSelf_c:ident_c` NA
## `MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## `MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## `MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## MFQSelf_c:`MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## `MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## t value
## (Intercept) 44.518
## MFQSelf_c -4.464
## `MFQSelf_c:inoutgroup_effects` NA
## `MFQSelf_c:ident_c` NA
## `MFQSelf_c:inoutgroup_effects:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects` NA
## MFQSelf_c:`MFQSelf_c:ident_c` NA
## `MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## `MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## `MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## MFQSelf_c:`MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## `MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## Pr(>|t|)
## (Intercept) < 0.0000000000000002
## MFQSelf_c 0.00000943
## `MFQSelf_c:inoutgroup_effects` NA
## `MFQSelf_c:ident_c` NA
## `MFQSelf_c:inoutgroup_effects:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects` NA
## MFQSelf_c:`MFQSelf_c:ident_c` NA
## `MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## `MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## `MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## MFQSelf_c:`MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## `MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
##
## (Intercept) ***
## MFQSelf_c ***
## `MFQSelf_c:inoutgroup_effects`
## `MFQSelf_c:ident_c`
## `MFQSelf_c:inoutgroup_effects:ident_c`
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects`
## MFQSelf_c:`MFQSelf_c:ident_c`
## `MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c`
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects:ident_c`
## `MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:inoutgroup_effects:ident_c`
## `MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c`
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c`
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:inoutgroup_effects:ident_c`
## MFQSelf_c:`MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c`
## `MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c`
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c`
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.934 on 667 degrees of freedom
## Multiple R-squared: 0.02901, Adjusted R-squared: 0.02756
## F-statistic: 19.93 on 1 and 667 DF, p-value: 0.000009426
ggplot(downstream_coeffs_bind,aes(MFQSelf_c, Contentious)) +
geom_smooth(method = "lm") +
labs(title = "Contentiousness predicted by projection coefficient",
subtitle = "Coefficient from initial study with binding data only controlling for stereotyping",
x = "Projection coefficient",
y = "Contentiousness") +
theme_minimal()
content_coeff_indiv <- lm(Contentious ~ MFQSelf_c*`MFQSelf_c:inoutgroup_effects`*`MFQSelf_c:ident_c`*`MFQSelf_c:inoutgroup_effects:ident_c`, data = downstream_coeffs_indiv)
summary(content_coeff_indiv)
##
## Call:
## lm(formula = Contentious ~ MFQSelf_c * `MFQSelf_c:inoutgroup_effects` *
## `MFQSelf_c:ident_c` * `MFQSelf_c:inoutgroup_effects:ident_c`,
## data = downstream_coeffs_indiv)
##
## Residuals:
## Min 1Q Median 3Q Max
## -3.8421 -1.7297 0.0059 1.7249 4.0858
##
## Coefficients: (14 not defined because of singularities)
## Estimate
## (Intercept) 4.3884
## MFQSelf_c -1.7047
## `MFQSelf_c:inoutgroup_effects` NA
## `MFQSelf_c:ident_c` NA
## `MFQSelf_c:inoutgroup_effects:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects` NA
## MFQSelf_c:`MFQSelf_c:ident_c` NA
## `MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## `MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## `MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## MFQSelf_c:`MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## `MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## Std. Error
## (Intercept) 0.1175
## MFQSelf_c 0.4285
## `MFQSelf_c:inoutgroup_effects` NA
## `MFQSelf_c:ident_c` NA
## `MFQSelf_c:inoutgroup_effects:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects` NA
## MFQSelf_c:`MFQSelf_c:ident_c` NA
## `MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## `MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## `MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## MFQSelf_c:`MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## `MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## t value
## (Intercept) 37.339
## MFQSelf_c -3.978
## `MFQSelf_c:inoutgroup_effects` NA
## `MFQSelf_c:ident_c` NA
## `MFQSelf_c:inoutgroup_effects:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects` NA
## MFQSelf_c:`MFQSelf_c:ident_c` NA
## `MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## `MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## `MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## MFQSelf_c:`MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## `MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## Pr(>|t|)
## (Intercept) < 0.0000000000000002
## MFQSelf_c 0.0000771
## `MFQSelf_c:inoutgroup_effects` NA
## `MFQSelf_c:ident_c` NA
## `MFQSelf_c:inoutgroup_effects:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects` NA
## MFQSelf_c:`MFQSelf_c:ident_c` NA
## `MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## `MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## `MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## MFQSelf_c:`MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## `MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
##
## (Intercept) ***
## MFQSelf_c ***
## `MFQSelf_c:inoutgroup_effects`
## `MFQSelf_c:ident_c`
## `MFQSelf_c:inoutgroup_effects:ident_c`
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects`
## MFQSelf_c:`MFQSelf_c:ident_c`
## `MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c`
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects:ident_c`
## `MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:inoutgroup_effects:ident_c`
## `MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c`
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c`
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:inoutgroup_effects:ident_c`
## MFQSelf_c:`MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c`
## `MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c`
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c`
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.939 on 667 degrees of freedom
## Multiple R-squared: 0.02317, Adjusted R-squared: 0.02171
## F-statistic: 15.82 on 1 and 667 DF, p-value: 0.00007712
ggplot(downstream_coeffs_indiv,aes(MFQSelf_c, Contentious)) +
geom_smooth(method = "lm") +
labs(title = "Contentiousness predicted by projection coefficient",
subtitle = "Coefficient from initial study with individuating data only controlling for stereotyping",
x = "Projection coefficient",
y = "Contentiousness") +
theme_minimal()
The models for individuating and binding data don’t differ much from the overall model.
treat_coeffs <- lm(Treatment ~ MFQSelf_c*`MFQSelf_c:inoutgroup_effects`*`MFQSelf_c:ident_c`*`MFQSelf_c:inoutgroup_effects:ident_c`, data = downstream_coeffs)
summary(treat_coeffs)
##
## Call:
## lm(formula = Treatment ~ MFQSelf_c * `MFQSelf_c:inoutgroup_effects` *
## `MFQSelf_c:ident_c` * `MFQSelf_c:inoutgroup_effects:ident_c`,
## data = downstream_coeffs)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.1786 -1.4518 0.4243 1.5159 3.6319
##
## Coefficients: (14 not defined because of singularities)
## Estimate
## (Intercept) 4.53122
## MFQSelf_c 2.23461
## `MFQSelf_c:inoutgroup_effects` NA
## `MFQSelf_c:ident_c` NA
## `MFQSelf_c:inoutgroup_effects:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects` NA
## MFQSelf_c:`MFQSelf_c:ident_c` NA
## `MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## `MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## `MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## MFQSelf_c:`MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## `MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## Std. Error
## (Intercept) 0.08175
## MFQSelf_c 0.28187
## `MFQSelf_c:inoutgroup_effects` NA
## `MFQSelf_c:ident_c` NA
## `MFQSelf_c:inoutgroup_effects:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects` NA
## MFQSelf_c:`MFQSelf_c:ident_c` NA
## `MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## `MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## `MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## MFQSelf_c:`MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## `MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## t value
## (Intercept) 55.428
## MFQSelf_c 7.928
## `MFQSelf_c:inoutgroup_effects` NA
## `MFQSelf_c:ident_c` NA
## `MFQSelf_c:inoutgroup_effects:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects` NA
## MFQSelf_c:`MFQSelf_c:ident_c` NA
## `MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## `MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## `MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## MFQSelf_c:`MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## `MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## Pr(>|t|)
## (Intercept) < 0.0000000000000002
## MFQSelf_c 0.0000000000000094
## `MFQSelf_c:inoutgroup_effects` NA
## `MFQSelf_c:ident_c` NA
## `MFQSelf_c:inoutgroup_effects:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects` NA
## MFQSelf_c:`MFQSelf_c:ident_c` NA
## `MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## `MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## `MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## MFQSelf_c:`MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## `MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
##
## (Intercept) ***
## MFQSelf_c ***
## `MFQSelf_c:inoutgroup_effects`
## `MFQSelf_c:ident_c`
## `MFQSelf_c:inoutgroup_effects:ident_c`
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects`
## MFQSelf_c:`MFQSelf_c:ident_c`
## `MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c`
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects:ident_c`
## `MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:inoutgroup_effects:ident_c`
## `MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c`
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c`
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:inoutgroup_effects:ident_c`
## MFQSelf_c:`MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c`
## `MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c`
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c`
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.84 on 667 degrees of freedom
## (5 observations deleted due to missingness)
## Multiple R-squared: 0.08611, Adjusted R-squared: 0.08474
## F-statistic: 62.85 on 1 and 667 DF, p-value: 0.000000000000009398
ggplot(downstream_coeffs,aes(MFQSelf_c, Treatment)) +
geom_smooth(method = "lm") +
labs(title = "Treatment predicted by projection coefficient",
subtitle = "Coefficient from initial study controlling for stereotyping",
x = "Projection coefficient") +
theme_minimal()
treat_codiff <- lm(Treatment ~ MFQSelf_c*proj_diff, coeffs_and_diffs)
summary(treat_codiff)
##
## Call:
## lm(formula = Treatment ~ MFQSelf_c * proj_diff, data = coeffs_and_diffs)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.9667 -1.3728 0.4417 1.4476 3.9042
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.0318 0.1330 37.833 < 0.0000000000000002 ***
## MFQSelf_c 1.9712 0.4591 4.294 0.000020189 ***
## proj_diff -0.7737 0.1503 -5.149 0.000000345 ***
## MFQSelf_c:proj_diff -0.2071 0.5857 -0.354 0.724
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.797 on 665 degrees of freedom
## (5 observations deleted due to missingness)
## Multiple R-squared: 0.1306, Adjusted R-squared: 0.1267
## F-statistic: 33.3 on 3 and 665 DF, p-value: < 0.00000000000000022
Both have main effects but the interaction is not significant.
treat_coeff_bind <- lm(Treatment ~ MFQSelf_c*`MFQSelf_c:inoutgroup_effects`*`MFQSelf_c:ident_c`*`MFQSelf_c:inoutgroup_effects:ident_c`, data = downstream_coeffs_bind)
summary(treat_coeff_bind)
##
## Call:
## lm(formula = Treatment ~ MFQSelf_c * `MFQSelf_c:inoutgroup_effects` *
## `MFQSelf_c:ident_c` * `MFQSelf_c:inoutgroup_effects:ident_c`,
## data = downstream_coeffs_bind)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.6927 -1.4613 0.4208 1.5608 3.1452
##
## Coefficients: (14 not defined because of singularities)
## Estimate
## (Intercept) 4.51005
## MFQSelf_c 2.70685
## `MFQSelf_c:inoutgroup_effects` NA
## `MFQSelf_c:ident_c` NA
## `MFQSelf_c:inoutgroup_effects:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects` NA
## MFQSelf_c:`MFQSelf_c:ident_c` NA
## `MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## `MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## `MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## MFQSelf_c:`MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## `MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## Std. Error
## (Intercept) 0.09391
## MFQSelf_c 0.47303
## `MFQSelf_c:inoutgroup_effects` NA
## `MFQSelf_c:ident_c` NA
## `MFQSelf_c:inoutgroup_effects:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects` NA
## MFQSelf_c:`MFQSelf_c:ident_c` NA
## `MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## `MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## `MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## MFQSelf_c:`MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## `MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## t value
## (Intercept) 48.026
## MFQSelf_c 5.722
## `MFQSelf_c:inoutgroup_effects` NA
## `MFQSelf_c:ident_c` NA
## `MFQSelf_c:inoutgroup_effects:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects` NA
## MFQSelf_c:`MFQSelf_c:ident_c` NA
## `MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## `MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## `MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## MFQSelf_c:`MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## `MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## Pr(>|t|)
## (Intercept) < 0.0000000000000002
## MFQSelf_c 0.0000000159
## `MFQSelf_c:inoutgroup_effects` NA
## `MFQSelf_c:ident_c` NA
## `MFQSelf_c:inoutgroup_effects:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects` NA
## MFQSelf_c:`MFQSelf_c:ident_c` NA
## `MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## `MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## `MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## MFQSelf_c:`MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## `MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
##
## (Intercept) ***
## MFQSelf_c ***
## `MFQSelf_c:inoutgroup_effects`
## `MFQSelf_c:ident_c`
## `MFQSelf_c:inoutgroup_effects:ident_c`
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects`
## MFQSelf_c:`MFQSelf_c:ident_c`
## `MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c`
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects:ident_c`
## `MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:inoutgroup_effects:ident_c`
## `MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c`
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c`
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:inoutgroup_effects:ident_c`
## MFQSelf_c:`MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c`
## `MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c`
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c`
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.879 on 667 degrees of freedom
## Multiple R-squared: 0.0468, Adjusted R-squared: 0.04537
## F-statistic: 32.75 on 1 and 667 DF, p-value: 0.00000001587
ggplot(downstream_coeffs_bind,aes(MFQSelf_c, Treatment)) +
geom_smooth(method = "lm") +
labs(title = "Treatment predicted by projection coefficient",
subtitle = "Coefficient from initial study with binding data only controlling for stereotyping",
x = "Projection coefficient") +
theme_minimal()
treat_coeff_indiv <- lm(Treatment ~ MFQSelf_c*`MFQSelf_c:inoutgroup_effects`*`MFQSelf_c:ident_c`*`MFQSelf_c:inoutgroup_effects:ident_c`, data = downstream_coeffs_indiv)
summary(treat_coeff_indiv)
##
## Call:
## lm(formula = Treatment ~ MFQSelf_c * `MFQSelf_c:inoutgroup_effects` *
## `MFQSelf_c:ident_c` * `MFQSelf_c:inoutgroup_effects:ident_c`,
## data = downstream_coeffs_indiv)
##
## Residuals:
## Min 1Q Median 3Q Max
## -5.3567 -1.4758 0.4083 1.6451 3.2494
##
## Coefficients: (14 not defined because of singularities)
## Estimate
## (Intercept) 4.3638
## MFQSelf_c 2.3044
## `MFQSelf_c:inoutgroup_effects` NA
## `MFQSelf_c:ident_c` NA
## `MFQSelf_c:inoutgroup_effects:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects` NA
## MFQSelf_c:`MFQSelf_c:ident_c` NA
## `MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## `MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## `MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## MFQSelf_c:`MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## `MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## Std. Error
## (Intercept) 0.1140
## MFQSelf_c 0.4158
## `MFQSelf_c:inoutgroup_effects` NA
## `MFQSelf_c:ident_c` NA
## `MFQSelf_c:inoutgroup_effects:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects` NA
## MFQSelf_c:`MFQSelf_c:ident_c` NA
## `MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## `MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## `MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## MFQSelf_c:`MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## `MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## t value
## (Intercept) 38.265
## MFQSelf_c 5.542
## `MFQSelf_c:inoutgroup_effects` NA
## `MFQSelf_c:ident_c` NA
## `MFQSelf_c:inoutgroup_effects:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects` NA
## MFQSelf_c:`MFQSelf_c:ident_c` NA
## `MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## `MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## `MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## MFQSelf_c:`MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## `MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## Pr(>|t|)
## (Intercept) < 0.0000000000000002
## MFQSelf_c 0.0000000431
## `MFQSelf_c:inoutgroup_effects` NA
## `MFQSelf_c:ident_c` NA
## `MFQSelf_c:inoutgroup_effects:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects` NA
## MFQSelf_c:`MFQSelf_c:ident_c` NA
## `MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## `MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## `MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## MFQSelf_c:`MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## `MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
##
## (Intercept) ***
## MFQSelf_c ***
## `MFQSelf_c:inoutgroup_effects`
## `MFQSelf_c:ident_c`
## `MFQSelf_c:inoutgroup_effects:ident_c`
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects`
## MFQSelf_c:`MFQSelf_c:ident_c`
## `MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c`
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects:ident_c`
## `MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:inoutgroup_effects:ident_c`
## `MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c`
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c`
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:inoutgroup_effects:ident_c`
## MFQSelf_c:`MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c`
## `MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c`
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c`
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.882 on 667 degrees of freedom
## Multiple R-squared: 0.04402, Adjusted R-squared: 0.04259
## F-statistic: 30.71 on 1 and 667 DF, p-value: 0.00000004311
ggplot(downstream_coeffs_indiv,aes(MFQSelf_c, Treatment)) +
geom_smooth(method = "lm") +
labs(title = "Treatment predicted by projection coefficient",
subtitle = "Coefficient from initial study with individuating data only controlling for stereotyping",
x = "Projection coefficient") +
theme_minimal()
Treatment effects with individuating and binding data look pretty much the same as the overall model.
valid_coeffs <- lm(Valid ~ MFQSelf_c*`MFQSelf_c:inoutgroup_effects`*`MFQSelf_c:ident_c`*`MFQSelf_c:inoutgroup_effects:ident_c`, data = downstream_coeffs)
summary(valid_coeffs)
##
## Call:
## lm(formula = Valid ~ MFQSelf_c * `MFQSelf_c:inoutgroup_effects` *
## `MFQSelf_c:ident_c` * `MFQSelf_c:inoutgroup_effects:ident_c`,
## data = downstream_coeffs)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.839 -1.548 0.519 1.595 3.420
##
## Coefficients: (14 not defined because of singularities)
## Estimate
## (Intercept) 4.53539
## MFQSelf_c 2.14270
## `MFQSelf_c:inoutgroup_effects` NA
## `MFQSelf_c:ident_c` NA
## `MFQSelf_c:inoutgroup_effects:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects` NA
## MFQSelf_c:`MFQSelf_c:ident_c` NA
## `MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## `MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## `MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## MFQSelf_c:`MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## `MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## Std. Error
## (Intercept) 0.08455
## MFQSelf_c 0.29152
## `MFQSelf_c:inoutgroup_effects` NA
## `MFQSelf_c:ident_c` NA
## `MFQSelf_c:inoutgroup_effects:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects` NA
## MFQSelf_c:`MFQSelf_c:ident_c` NA
## `MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## `MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## `MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## MFQSelf_c:`MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## `MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## t value
## (Intercept) 53.64
## MFQSelf_c 7.35
## `MFQSelf_c:inoutgroup_effects` NA
## `MFQSelf_c:ident_c` NA
## `MFQSelf_c:inoutgroup_effects:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects` NA
## MFQSelf_c:`MFQSelf_c:ident_c` NA
## `MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## `MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## `MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## MFQSelf_c:`MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## `MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## Pr(>|t|)
## (Intercept) < 0.0000000000000002
## MFQSelf_c 0.000000000000582
## `MFQSelf_c:inoutgroup_effects` NA
## `MFQSelf_c:ident_c` NA
## `MFQSelf_c:inoutgroup_effects:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects` NA
## MFQSelf_c:`MFQSelf_c:ident_c` NA
## `MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## `MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## `MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## MFQSelf_c:`MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## `MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
##
## (Intercept) ***
## MFQSelf_c ***
## `MFQSelf_c:inoutgroup_effects`
## `MFQSelf_c:ident_c`
## `MFQSelf_c:inoutgroup_effects:ident_c`
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects`
## MFQSelf_c:`MFQSelf_c:ident_c`
## `MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c`
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects:ident_c`
## `MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:inoutgroup_effects:ident_c`
## `MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c`
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c`
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:inoutgroup_effects:ident_c`
## MFQSelf_c:`MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c`
## `MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c`
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c`
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.903 on 667 degrees of freedom
## (5 observations deleted due to missingness)
## Multiple R-squared: 0.07493, Adjusted R-squared: 0.07354
## F-statistic: 54.02 on 1 and 667 DF, p-value: 0.0000000000005815
ggplot(downstream_coeffs,aes(MFQSelf_c, Valid)) +
geom_smooth(method = "lm") +
labs(title = "Validity of target's position predicted by projection coefficient",
subtitle = "Coefficient from initial study controlling for stereotyping",
x = "Projection coefficient",
y = "Validity") +
theme_minimal()
valid_codiff <- lm(Valid ~ MFQSelf_c*proj_diff, coeffs_and_diffs)
summary(valid_codiff)
##
## Call:
## lm(formula = Valid ~ MFQSelf_c * proj_diff, data = coeffs_and_diffs)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.9623 -1.3315 0.5359 1.4941 4.0047
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 5.0766 0.1377 36.863 < 0.0000000000000002 ***
## MFQSelf_c 1.6560 0.4754 3.484 0.000527 ***
## proj_diff -0.8204 0.1556 -5.273 0.000000181 ***
## MFQSelf_c:proj_diff 0.1479 0.6065 0.244 0.807380
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.861 on 665 degrees of freedom
## (5 observations deleted due to missingness)
## Multiple R-squared: 0.1179, Adjusted R-squared: 0.1139
## F-statistic: 29.63 on 3 and 665 DF, p-value: < 0.00000000000000022
Both have main effects but the interaction is not significant.
valid_coeff_bind <- lm(Valid ~ MFQSelf_c*`MFQSelf_c:inoutgroup_effects`*`MFQSelf_c:ident_c`*`MFQSelf_c:inoutgroup_effects:ident_c`, data = downstream_coeffs_bind)
summary(valid_coeff_bind)
##
## Call:
## lm(formula = Valid ~ MFQSelf_c * `MFQSelf_c:inoutgroup_effects` *
## `MFQSelf_c:ident_c` * `MFQSelf_c:inoutgroup_effects:ident_c`,
## data = downstream_coeffs_bind)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.7196 -1.4663 0.4361 1.6767 2.9226
##
## Coefficients: (14 not defined because of singularities)
## Estimate
## (Intercept) 4.54937
## MFQSelf_c 2.32294
## `MFQSelf_c:inoutgroup_effects` NA
## `MFQSelf_c:ident_c` NA
## `MFQSelf_c:inoutgroup_effects:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects` NA
## MFQSelf_c:`MFQSelf_c:ident_c` NA
## `MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## `MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## `MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## MFQSelf_c:`MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## `MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## Std. Error
## (Intercept) 0.09725
## MFQSelf_c 0.48986
## `MFQSelf_c:inoutgroup_effects` NA
## `MFQSelf_c:ident_c` NA
## `MFQSelf_c:inoutgroup_effects:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects` NA
## MFQSelf_c:`MFQSelf_c:ident_c` NA
## `MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## `MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## `MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## MFQSelf_c:`MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## `MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## t value
## (Intercept) 46.780
## MFQSelf_c 4.742
## `MFQSelf_c:inoutgroup_effects` NA
## `MFQSelf_c:ident_c` NA
## `MFQSelf_c:inoutgroup_effects:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects` NA
## MFQSelf_c:`MFQSelf_c:ident_c` NA
## `MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## `MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## `MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## MFQSelf_c:`MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## `MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## Pr(>|t|)
## (Intercept) < 0.0000000000000002
## MFQSelf_c 0.00000259
## `MFQSelf_c:inoutgroup_effects` NA
## `MFQSelf_c:ident_c` NA
## `MFQSelf_c:inoutgroup_effects:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects` NA
## MFQSelf_c:`MFQSelf_c:ident_c` NA
## `MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## `MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## `MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## MFQSelf_c:`MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## `MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
##
## (Intercept) ***
## MFQSelf_c ***
## `MFQSelf_c:inoutgroup_effects`
## `MFQSelf_c:ident_c`
## `MFQSelf_c:inoutgroup_effects:ident_c`
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects`
## MFQSelf_c:`MFQSelf_c:ident_c`
## `MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c`
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects:ident_c`
## `MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:inoutgroup_effects:ident_c`
## `MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c`
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c`
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:inoutgroup_effects:ident_c`
## MFQSelf_c:`MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c`
## `MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c`
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c`
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.946 on 667 degrees of freedom
## Multiple R-squared: 0.03261, Adjusted R-squared: 0.03116
## F-statistic: 22.49 on 1 and 667 DF, p-value: 0.000002589
ggplot(downstream_coeffs_bind,aes(MFQSelf_c, Valid)) +
geom_smooth(method = "lm") +
labs(title = "Validity predicted by projection coefficient",
subtitle = "Coefficient from initial study with binding data only controlling for stereotyping",
x = "Projection coefficient",
y = "Validity") +
theme_minimal()
valid_coeff_indiv <- lm(Valid ~ MFQSelf_c*`MFQSelf_c:inoutgroup_effects`*`MFQSelf_c:ident_c`*`MFQSelf_c:inoutgroup_effects:ident_c`, data = downstream_coeffs_indiv)
summary(valid_coeff_indiv)
##
## Call:
## lm(formula = Valid ~ MFQSelf_c * `MFQSelf_c:inoutgroup_effects` *
## `MFQSelf_c:ident_c` * `MFQSelf_c:inoutgroup_effects:ident_c`,
## data = downstream_coeffs_indiv)
##
## Residuals:
## Min 1Q Median 3Q Max
## -4.622 -1.452 0.492 1.731 3.061
##
## Coefficients: (14 not defined because of singularities)
## Estimate
## (Intercept) 4.4421
## MFQSelf_c 1.8915
## `MFQSelf_c:inoutgroup_effects` NA
## `MFQSelf_c:ident_c` NA
## `MFQSelf_c:inoutgroup_effects:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects` NA
## MFQSelf_c:`MFQSelf_c:ident_c` NA
## `MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## `MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## `MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## MFQSelf_c:`MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## `MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## Std. Error
## (Intercept) 0.1182
## MFQSelf_c 0.4310
## `MFQSelf_c:inoutgroup_effects` NA
## `MFQSelf_c:ident_c` NA
## `MFQSelf_c:inoutgroup_effects:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects` NA
## MFQSelf_c:`MFQSelf_c:ident_c` NA
## `MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## `MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## `MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## MFQSelf_c:`MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## `MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## t value
## (Intercept) 37.579
## MFQSelf_c 4.389
## `MFQSelf_c:inoutgroup_effects` NA
## `MFQSelf_c:ident_c` NA
## `MFQSelf_c:inoutgroup_effects:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects` NA
## MFQSelf_c:`MFQSelf_c:ident_c` NA
## `MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## `MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## `MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## MFQSelf_c:`MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## `MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## Pr(>|t|)
## (Intercept) < 0.0000000000000002
## MFQSelf_c 0.0000133
## `MFQSelf_c:inoutgroup_effects` NA
## `MFQSelf_c:ident_c` NA
## `MFQSelf_c:inoutgroup_effects:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects` NA
## MFQSelf_c:`MFQSelf_c:ident_c` NA
## `MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## `MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## `MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## MFQSelf_c:`MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## `MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c` NA
##
## (Intercept) ***
## MFQSelf_c ***
## `MFQSelf_c:inoutgroup_effects`
## `MFQSelf_c:ident_c`
## `MFQSelf_c:inoutgroup_effects:ident_c`
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects`
## MFQSelf_c:`MFQSelf_c:ident_c`
## `MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c`
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects:ident_c`
## `MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:inoutgroup_effects:ident_c`
## `MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c`
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c`
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:inoutgroup_effects:ident_c`
## MFQSelf_c:`MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c`
## `MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c`
## MFQSelf_c:`MFQSelf_c:inoutgroup_effects`:`MFQSelf_c:ident_c`:`MFQSelf_c:inoutgroup_effects:ident_c`
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 1.951 on 667 degrees of freedom
## Multiple R-squared: 0.02807, Adjusted R-squared: 0.02661
## F-statistic: 19.26 on 1 and 667 DF, p-value: 0.00001326
ggplot(downstream_coeffs_indiv,aes(MFQSelf_c, Valid)) +
geom_smooth(method = "lm") +
labs(title = "Validity predicted by projection coefficient",
subtitle = "Coefficient from initial study with individuating data only controlling for stereotyping",
x = "Projection coefficient",
y = "Validity") +
theme_minimal()
Models using the individuating and binding data look the same as the overall model.
Does this mean looking at whether they are approve or disapprove of the condition in F1 and F2? Could do liberal leaning vs conservative leaning, minus PB and third party
In the original output for initial study and follow-up 1, already done.